In [ ]:
num_iterations = 200
print("The number of iterations is: ", num_iterations)
The number of iterations is: 200
In [ ]:
import numpy as np
import numpy.matlib as matlib
from libsvm.svmutil import *
import matplotlib.pyplot as plt
def data(N,sigma):
w = np.ones(10)/np.sqrt(10)
w1 = [1., 1., 1., 1., 1., -1., -1., -1., -1., -1.]/np.sqrt(10)
w2 = [-1., -1., 0, 1., 1., -1., -1., 0, -1., -1.]/np.sqrt(8)
x = np.zeros((4,10))
x[1,:] = x[0,:] + sigma*w1
x[2,:] = x[0,:] + sigma*w2
x[3,:] = x[2,:] + sigma*w1
X1 = x + sigma*matlib.repmat(w,4,1)/2
X2 = x - sigma*matlib.repmat(w,4,1)/2
X1 = matlib.repmat(X1,2*N,1)
X2 = matlib.repmat(X2,2*N,1)
X = np.concatenate((X1, X2), axis=0)
Y = np.concatenate((np.ones(4*2*N), -np.ones(4*2*N)),axis=0)
Z = np.random.permutation(16*N)
Z = Z[:N]
X = X[Z,:]
X = X + 0.2*sigma*np.random.randn(N,10)
Y = Y[Z]
return X, Y
# Task 2a: Generating Parameter Values
lambda_values = np.logspace(-2, 1, 20) # Logarithmically spaced values between 0.01 and 10
# Initialize arrays to store errors
training_errors = []
test_errors = []
sigma = 0.5
# Task 2b-d: Training, Testing, and Repeating the Experiment
#num_iterations = 100
for i in range(num_iterations):
# Generate data
X_train, y_train = data(100,sigma)
X_test, y_test = data(1000, sigma)
for lam in lambda_values:
# Train SVM
svm_problem_setup = svm_problem(y_train.tolist(), X_train.tolist())
param = svm_parameter(f'-t 0 -c {lam}')
model = svm_train(svm_problem_setup, param)
# Predict on training and test data
i, train_accuracy, i = svm_predict(y_train.tolist(), X_train.tolist(), model)
i, test_accuracy, i = svm_predict(y_test.tolist(), X_test.tolist(), model)
# Calculate errors
training_errors.append(100 - train_accuracy[0]) # Convert to error percentage
test_errors.append(100 - test_accuracy[0]) # Convert to error percentage
# Task 2e: Averaging Errors and Plotting
training_errors = np.array(training_errors).reshape(num_iterations, -1)
test_errors = np.array(test_errors).reshape(num_iterations, -1)
avg_training_error = np.mean(training_errors, axis=0)
avg_test_error = np.mean(test_errors, axis=0)
lambda_values_log = np.log10(lambda_values)
# Plotting
plt.figure(figsize=(10, 6))
plt.plot(lambda_values_log, avg_training_error, label='R_empirical (Average Training Error)')
plt.plot(lambda_values_log, avg_test_error, label='R_actual (Average Test Error)')
plt.plot(lambda_values_log, avg_test_error - avg_training_error, label='R_structural (Difference)')
plt.xlabel('log(λ)')
plt.ylabel('Error (%)')
plt.title('Risks vs. λ (0.01,10) @ σ = 0.5')
plt.legend()
plt.show()
* optimization finished, #iter = 48 nu = 0.920000 obj = -0.894241, rho = 0.872642 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -1.270081, rho = 0.816829 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -1.793336, rho = 0.736518 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -2.510079, rho = 0.622441 nSV = 94, nBSV = 91 Total nSV = 94 Accuracy = 54% (54/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -3.466723, rho = 0.456901 nSV = 94, nBSV = 91 Total nSV = 94 Accuracy = 55% (55/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -4.688957, rho = 0.218779 nSV = 94, nBSV = 91 Total nSV = 94 Accuracy = 76% (76/100) (classification) Accuracy = 74% (740/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -6.138730, rho = -0.043379 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 94% (94/100) (classification) Accuracy = 92.3% (923/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -7.782803, rho = -0.122397 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 99% (99/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 38 nu = 0.740000 obj = -9.737874, rho = -0.114800 nSV = 75, nBSV = 73 Total nSV = 75 Accuracy = 98% (98/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 46 nu = 0.653751 obj = -12.014190, rho = -0.055246 nSV = 69, nBSV = 62 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 36 nu = 0.562034 obj = -14.712235, rho = -0.013484 nSV = 58, nBSV = 54 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 36 nu = 0.481719 obj = -17.850051, rho = 0.005457 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 83 nu = 0.407737 obj = -21.516851, rho = -0.034123 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 47 nu = 0.339878 obj = -26.038311, rho = -0.106560 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 44 nu = 0.284277 obj = -31.580581, rho = -0.104549 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 43 nu = 0.243334 obj = -38.019377, rho = -0.091532 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 93 nu = 0.202632 obj = -45.293761, rho = -0.029927 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.168929 obj = -54.261038, rho = -0.062588 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 71 nu = 0.141692 obj = -65.156725, rho = -0.180296 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 66 nu = 0.119350 obj = -77.476837, rho = -0.259244 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.948416, rho = 0.812954 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 54.3% (543/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.344330, rho = 0.730943 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 54.3% (543/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.892535, rho = 0.612976 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 54.3% (543/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.637034, rho = 0.443285 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 54.3% (543/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.616781, rho = 0.199193 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 69% (69/100) (classification) Accuracy = 67.7% (677/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.837436, rho = -0.151921 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 87% (87/100) (classification) Accuracy = 92.2% (922/1000) (classification) * optimization finished, #iter = 49 nu = 0.932892 obj = -6.261974, rho = -0.276373 nSV = 94, nBSV = 91 Total nSV = 94 Accuracy = 95% (95/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -7.935809, rho = -0.200697 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 97% (97/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 57 nu = 0.768030 obj = -9.862578, rho = -0.211248 nSV = 78, nBSV = 73 Total nSV = 78 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 41 nu = 0.662336 obj = -12.146676, rho = -0.172374 nSV = 68, nBSV = 64 Total nSV = 68 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 38 nu = 0.570180 obj = -14.869296, rho = -0.146401 nSV = 58, nBSV = 54 Total nSV = 58 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 41 nu = 0.478976 obj = -18.199165, rho = -0.109274 nSV = 51, nBSV = 44 Total nSV = 51 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 41 nu = 0.411964 obj = -22.325856, rho = -0.071661 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 43 nu = 0.356517 obj = -27.083053, rho = -0.108234 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 36 nu = 0.298400 obj = -32.770027, rho = -0.143746 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 39 nu = 0.250586 obj = -39.426365, rho = -0.166812 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 37 nu = 0.217015 obj = -46.924552, rho = -0.116885 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.175244 obj = -55.321719, rho = -0.141231 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*....* optimization finished, #iter = 506 nu = 0.141854 obj = -65.666426, rho = -0.215708 nSV = 21, nBSV = 10 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.122682 obj = -78.181472, rho = -0.213607 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 44 nu = 0.780000 obj = -0.759544, rho = 0.905397 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 61% (61/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 44 nu = 0.780000 obj = -1.079664, rho = 0.863918 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 61% (61/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 45 nu = 0.780000 obj = -1.526349, rho = 0.805126 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 61% (61/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 46 nu = 0.780000 obj = -2.140345, rho = 0.719409 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 61% (61/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 44 nu = 0.780000 obj = -2.964493, rho = 0.596062 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 61% (61/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 44 nu = 0.780000 obj = -4.027802, rho = 0.418254 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 71% (71/100) (classification) Accuracy = 54.9% (549/1000) (classification) * optimization finished, #iter = 43 nu = 0.780000 obj = -5.304494, rho = 0.163188 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 91% (91/100) (classification) Accuracy = 81.3% (813/1000) (classification) * optimization finished, #iter = 40 nu = 0.736404 obj = -6.677423, rho = 0.010425 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 98% (98/100) (classification) Accuracy = 94% (940/1000) (classification) * optimization finished, #iter = 36 nu = 0.656987 obj = -8.206756, rho = 0.026977 nSV = 66, nBSV = 64 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 36 nu = 0.555385 obj = -9.967226, rho = 0.038011 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 29 nu = 0.488294 obj = -11.899243, rho = 0.189074 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 33 nu = 0.405242 obj = -13.867543, rho = 0.246108 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 35 nu = 0.324758 obj = -16.042484, rho = 0.256644 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 54 nu = 0.266024 obj = -18.518560, rho = 0.292320 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 65 nu = 0.215031 obj = -21.141831, rho = 0.344646 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 60 nu = 0.171222 obj = -23.949243, rho = 0.386644 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 75 nu = 0.132300 obj = -27.167771, rho = 0.387980 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 62 nu = 0.103808 obj = -31.110985, rho = 0.458295 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 98 nu = 0.082365 obj = -35.658619, rho = 0.521824 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 78 nu = 0.066658 obj = -41.150112, rho = 0.550173 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -0.934661, rho = -0.922823 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 53.3% (533/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.328482, rho = -0.888985 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 53.3% (533/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.877887, rho = -0.840311 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 53.3% (533/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.632827, rho = -0.770296 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 53.3% (533/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.645618, rho = -0.669582 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 54% (540/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.951109, rho = -0.524710 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 74% (74/100) (classification) Accuracy = 74.8% (748/1000) (classification) * optimization finished, #iter = 49 nu = 0.957032 obj = -6.515934, rho = -0.321408 nSV = 96, nBSV = 93 Total nSV = 96 Accuracy = 95% (95/100) (classification) Accuracy = 94.2% (942/1000) (classification) * optimization finished, #iter = 44 nu = 0.866012 obj = -8.368515, rho = -0.281973 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 42 nu = 0.786355 obj = -10.632172, rho = -0.219484 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 47 nu = 0.705741 obj = -13.280602, rho = -0.254162 nSV = 73, nBSV = 66 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 36 nu = 0.620000 obj = -16.445226, rho = -0.169077 nSV = 63, nBSV = 60 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 33 nu = 0.542998 obj = -20.075948, rho = -0.105843 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.458270 obj = -24.076281, rho = -0.144930 nSV = 49, nBSV = 40 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 56 nu = 0.383723 obj = -28.987748, rho = -0.193480 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 58 nu = 0.319162 obj = -35.121374, rho = -0.094988 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 89 nu = 0.261534 obj = -42.811443, rho = -0.113216 nSV = 32, nBSV = 23 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 86 nu = 0.223766 obj = -52.795724, rho = -0.258573 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 77 nu = 0.193583 obj = -65.104725, rho = -0.264434 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 78 nu = 0.165780 obj = -80.199580, rho = -0.351939 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 256 nu = 0.143303 obj = -97.849654, rho = -0.302076 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -0.840074, rho = -0.939555 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -1.195837, rho = -0.913053 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -1.694148, rho = -0.874931 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -2.383142, rho = -0.820094 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -3.316701, rho = -0.741214 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -4.540553, rho = -0.627749 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 59% (59/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -6.054720, rho = -0.464536 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 90% (90/100) (classification) Accuracy = 77.6% (776/1000) (classification) * optimization finished, #iter = 49 nu = 0.841330 obj = -7.729993, rho = -0.256635 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 96% (96/100) (classification) Accuracy = 94.1% (941/1000) (classification) * optimization finished, #iter = 48 nu = 0.746244 obj = -9.554298, rho = -0.190284 nSV = 78, nBSV = 73 Total nSV = 78 Accuracy = 96% (96/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 42 nu = 0.644040 obj = -11.726645, rho = -0.170471 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 39 nu = 0.560317 obj = -14.236850, rho = -0.254691 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 46 nu = 0.459922 obj = -17.228126, rho = -0.284998 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 50 nu = 0.386644 obj = -21.004899, rho = -0.261024 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 37 nu = 0.327099 obj = -25.739756, rho = -0.302342 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 53 nu = 0.282056 obj = -31.702616, rho = -0.252171 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 55 nu = 0.243085 obj = -38.626377, rho = -0.286968 nSV = 26, nBSV = 21 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 37 nu = 0.205021 obj = -46.792205, rho = -0.170775 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 70 nu = 0.174037 obj = -56.267062, rho = -0.155347 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 98 nu = 0.140773 obj = -68.282727, rho = -0.208296 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.118374 obj = -84.738231, rho = -0.404139 nSV = 15, nBSV = 10 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -0.909809, rho = 0.836987 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.289674, rho = 0.765514 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.815734, rho = 0.662704 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.530322, rho = 0.514817 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.471065, rho = 0.302088 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 62% (62/100) (classification) Accuracy = 56.2% (562/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.643939, rho = -0.003911 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 85% (85/100) (classification) Accuracy = 83.5% (835/1000) (classification) * optimization finished, #iter = 48 nu = 0.901669 obj = -6.000473, rho = -0.201868 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 94% (94/100) (classification) Accuracy = 92.9% (929/1000) (classification) * optimization finished, #iter = 45 nu = 0.821057 obj = -7.581915, rho = -0.207237 nSV = 84, nBSV = 82 Total nSV = 84 Accuracy = 95% (95/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 45 nu = 0.731621 obj = -9.429145, rho = -0.090792 nSV = 75, nBSV = 71 Total nSV = 75 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 55 nu = 0.637213 obj = -11.525950, rho = -0.129124 nSV = 66, nBSV = 61 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 37 nu = 0.545823 obj = -13.980868, rho = -0.073426 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 37 nu = 0.458885 obj = -16.930453, rho = -0.152597 nSV = 47, nBSV = 44 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 81 nu = 0.385013 obj = -20.405449, rho = -0.162059 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.326267 obj = -24.517639, rho = -0.150989 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 54 nu = 0.271288 obj = -29.483777, rho = -0.149807 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 55 nu = 0.225999 obj = -35.507207, rho = -0.185487 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 67 nu = 0.195609 obj = -42.394652, rho = -0.136595 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 59 nu = 0.164290 obj = -49.359129, rho = -0.122313 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 91 nu = 0.136227 obj = -56.366992, rho = -0.111068 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 68 nu = 0.104493 obj = -63.596241, rho = -0.033529 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -0.916683, rho = -0.934706 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.303897, rho = -0.906078 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.845163, rho = -0.864898 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -2.591214, rho = -0.805662 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -3.597059, rho = -0.720455 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.904638, rho = -0.597888 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 59% (59/100) (classification) Accuracy = 56.4% (564/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.497335, rho = -0.421583 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 95% (95/100) (classification) Accuracy = 87.1% (871/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -8.246447, rho = -0.295005 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 97% (97/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 46 nu = 0.808124 obj = -10.132165, rho = -0.194577 nSV = 83, nBSV = 79 Total nSV = 83 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 44 nu = 0.685783 obj = -12.282934, rho = -0.158121 nSV = 72, nBSV = 67 Total nSV = 72 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 37 nu = 0.575159 obj = -14.884348, rho = -0.200747 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 30 nu = 0.490737 obj = -17.953213, rho = -0.227047 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 52 nu = 0.407676 obj = -21.646414, rho = -0.284420 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 36 nu = 0.337897 obj = -26.222694, rho = -0.302113 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 77 nu = 0.287292 obj = -32.045853, rho = -0.229780 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.247966 obj = -38.919225, rho = -0.355756 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 79 nu = 0.209298 obj = -46.597206, rho = -0.393685 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*..* optimization finished, #iter = 360 nu = 0.171726 obj = -56.018526, rho = -0.398909 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 87 nu = 0.140570 obj = -68.398866, rho = -0.384777 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 53 nu = 0.125344 obj = -83.573273, rho = -0.290513 nSV = 15, nBSV = 10 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -0.927941, rho = -0.907083 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.314578, rho = -0.866344 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.849119, rho = -0.807742 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -2.573301, rho = -0.723446 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -3.522451, rho = -0.602191 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 59% (59/100) (classification) Accuracy = 52.4% (524/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -4.696260, rho = -0.427772 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 83% (83/100) (classification) Accuracy = 80.1% (801/1000) (classification) * optimization finished, #iter = 46 nu = 0.916624 obj = -6.018205, rho = -0.310501 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 96% (96/100) (classification) Accuracy = 93.3% (933/1000) (classification) * optimization finished, #iter = 41 nu = 0.820000 obj = -7.565467, rho = -0.277969 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 99% (99/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 41 nu = 0.732786 obj = -9.348153, rho = -0.180532 nSV = 74, nBSV = 72 Total nSV = 74 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 42 nu = 0.635640 obj = -11.380150, rho = -0.105362 nSV = 66, nBSV = 60 Total nSV = 66 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 33 nu = 0.529927 obj = -13.850504, rho = -0.143259 nSV = 55, nBSV = 52 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 58 nu = 0.467033 obj = -16.550410, rho = -0.084122 nSV = 49, nBSV = 42 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.385120 obj = -19.502439, rho = -0.040653 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 74 nu = 0.321943 obj = -22.776121, rho = 0.036404 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 35 nu = 0.260776 obj = -26.554721, rho = 0.090139 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 67 nu = 0.212261 obj = -30.622655, rho = 0.051763 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 71 nu = 0.172274 obj = -35.254337, rho = -0.047647 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.134883 obj = -40.343656, rho = -0.015500 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 149 nu = 0.105588 obj = -46.762458, rho = 0.022949 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 79 nu = 0.084267 obj = -55.302640, rho = 0.080553 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -0.948418, rho = -0.883717 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.344334, rho = -0.832733 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.892543, rho = -0.759395 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -2.637052, rho = -0.653901 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -3.616817, rho = -0.502155 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 59% (59/100) (classification) Accuracy = 55.2% (552/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -4.837513, rho = -0.283874 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 89% (89/100) (classification) Accuracy = 88.2% (882/1000) (classification) * optimization finished, #iter = 48 nu = 0.924171 obj = -6.257282, rho = -0.153905 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 98% (98/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 46 nu = 0.848765 obj = -7.959231, rho = -0.090605 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 45 nu = 0.764801 obj = -9.927219, rho = -0.022228 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 49 nu = 0.669207 obj = -12.181348, rho = -0.046503 nSV = 69, nBSV = 64 Total nSV = 69 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 38 nu = 0.567510 obj = -14.890042, rho = -0.006445 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.486208 obj = -18.237741, rho = -0.002157 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 81 nu = 0.413793 obj = -22.059228, rho = -0.019754 nSV = 46, nBSV = 38 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 42 nu = 0.346721 obj = -26.965106, rho = -0.043190 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.295563 obj = -32.778280, rho = -0.011321 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 49 nu = 0.253864 obj = -39.616180, rho = -0.076659 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.212160 obj = -47.393598, rho = -0.101700 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 90 nu = 0.175824 obj = -56.795206, rho = -0.123676 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 219 nu = 0.146618 obj = -67.937544, rho = -0.120196 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 153 nu = 0.128610 obj = -80.654166, rho = -0.144184 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -0.915296, rho = -0.939027 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.301026, rho = -0.912293 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.839223, rho = -0.873838 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.578924, rho = -0.818523 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.571629, rho = -0.738954 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 51.8% (518/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -4.852019, rho = -0.624499 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 75% (75/100) (classification) Accuracy = 70% (700/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -6.388461, rho = -0.473279 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 91% (91/100) (classification) Accuracy = 89.2% (892/1000) (classification) * optimization finished, #iter = 47 nu = 0.867682 obj = -8.155682, rho = -0.421749 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 95% (95/100) (classification) Accuracy = 94.5% (945/1000) (classification) * optimization finished, #iter = 43 nu = 0.772979 obj = -10.245545, rho = -0.424881 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 96% (96/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 39 nu = 0.693108 obj = -12.692485, rho = -0.506971 nSV = 70, nBSV = 67 Total nSV = 70 Accuracy = 96% (96/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 41 nu = 0.600000 obj = -15.517941, rho = -0.420187 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 50 nu = 0.502441 obj = -18.881726, rho = -0.348684 nSV = 56, nBSV = 48 Total nSV = 56 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 47 nu = 0.426587 obj = -22.970597, rho = -0.287179 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 62 nu = 0.357157 obj = -28.093121, rho = -0.261932 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 38 nu = 0.303520 obj = -34.678873, rho = -0.185043 nSV = 32, nBSV = 29 Total nSV = 32 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 73 nu = 0.257527 obj = -42.873623, rho = -0.173817 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 94 nu = 0.221990 obj = -52.771545, rho = -0.269947 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 95 nu = 0.184931 obj = -66.056904, rho = -0.301927 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 66 nu = 0.160425 obj = -84.158755, rho = -0.307251 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..* optimization finished, #iter = 264 nu = 0.142934 obj = -107.397512, rho = -0.304593 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -0.932148, rho = -0.914794 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.323283, rho = -0.877436 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.867131, rho = -0.823698 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.610569, rho = -0.746398 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.599565, rho = -0.635207 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 55% (55/100) (classification) Accuracy = 51.9% (519/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.855818, rho = -0.475263 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 83% (83/100) (classification) Accuracy = 81.4% (814/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.341966, rho = -0.315748 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 94% (94/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -8.030328, rho = -0.296815 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 96% (96/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 45 nu = 0.769159 obj = -9.952788, rho = -0.265252 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 41 nu = 0.671453 obj = -12.267780, rho = -0.277554 nSV = 68, nBSV = 65 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.579430 obj = -14.915916, rho = -0.299330 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 64 nu = 0.490426 obj = -18.044064, rho = -0.298521 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.411144 obj = -21.874181, rho = -0.296040 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 34 nu = 0.354013 obj = -26.210630, rho = -0.286874 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 58 nu = 0.296901 obj = -31.083845, rho = -0.313256 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 52 nu = 0.241757 obj = -36.895500, rho = -0.317896 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.198421 obj = -43.694681, rho = -0.363681 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 47 nu = 0.167695 obj = -51.850784, rho = -0.448712 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 189 nu = 0.134875 obj = -60.500044, rho = -0.477883 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.108280 obj = -71.884556, rho = -0.443700 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -0.897147, rho = -0.936598 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.276087, rho = -0.908800 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.805765, rho = -0.868813 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -2.535794, rho = -0.811294 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -3.519930, rho = -0.728556 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -4.799051, rho = -0.609541 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 65% (65/100) (classification) Accuracy = 61.1% (611/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -6.356543, rho = -0.438344 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 91% (91/100) (classification) Accuracy = 90.8% (908/1000) (classification) * optimization finished, #iter = 49 nu = 0.888975 obj = -8.052154, rho = -0.294232 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 63 nu = 0.777408 obj = -9.944326, rho = -0.228332 nSV = 81, nBSV = 75 Total nSV = 81 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 45 nu = 0.663451 obj = -12.250412, rho = -0.190624 nSV = 69, nBSV = 64 Total nSV = 69 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 40 nu = 0.575234 obj = -15.087815, rho = -0.136746 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 41 nu = 0.489244 obj = -18.363353, rho = -0.131503 nSV = 52, nBSV = 45 Total nSV = 52 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 31 nu = 0.412476 obj = -22.399268, rho = -0.085199 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 42 nu = 0.365588 obj = -26.983772, rho = -0.221089 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 64 nu = 0.300122 obj = -31.813817, rho = -0.276481 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.244222 obj = -37.628723, rho = -0.266493 nSV = 31, nBSV = 21 Total nSV = 31 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 99 nu = 0.199987 obj = -45.056266, rho = -0.283644 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 92 nu = 0.162633 obj = -54.857666, rho = -0.315324 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.138468 obj = -67.290070, rho = -0.378727 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*....* optimization finished, #iter = 563 nu = 0.115400 obj = -83.526903, rho = -0.463148 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -0.943413, rho = 0.820812 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -1.333978, rho = 0.742247 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -1.871115, rho = 0.629235 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -2.592713, rho = 0.466674 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -3.525075, rho = 0.232837 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 66% (66/100) (classification) Accuracy = 63.6% (636/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.647685, rho = -0.103526 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 89% (89/100) (classification) Accuracy = 87.6% (876/1000) (classification) * optimization finished, #iter = 52 nu = 0.920000 obj = -5.901816, rho = -0.205669 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 97% (97/100) (classification) Accuracy = 94.2% (942/1000) (classification) * optimization finished, #iter = 44 nu = 0.825874 obj = -7.318062, rho = -0.216088 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 41 nu = 0.713909 obj = -8.946551, rho = -0.183119 nSV = 72, nBSV = 70 Total nSV = 72 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 62 nu = 0.605011 obj = -10.837280, rho = -0.216319 nSV = 64, nBSV = 58 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 32 nu = 0.505363 obj = -13.180487, rho = -0.265816 nSV = 53, nBSV = 49 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 40 nu = 0.429478 obj = -16.015038, rho = -0.251630 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 33 nu = 0.378526 obj = -19.298335, rho = -0.302634 nSV = 39, nBSV = 36 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 80 nu = 0.308033 obj = -22.837610, rho = -0.289786 nSV = 35, nBSV = 26 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 65 nu = 0.255647 obj = -27.103859, rho = -0.285095 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 32 nu = 0.219604 obj = -31.871619, rho = -0.153628 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.174898 obj = -36.658237, rho = -0.177715 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 45 nu = 0.139485 obj = -42.838159, rho = -0.125289 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 75 nu = 0.113816 obj = -49.377809, rho = -0.111099 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 61 nu = 0.092136 obj = -57.169360, rho = -0.152877 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -0.919273, rho = -0.916418 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 54% (540/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.309256, rho = -0.879771 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 54% (540/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.856250, rho = -0.827057 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 54% (540/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -2.614156, rho = -0.751230 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 54% (540/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -3.644528, rho = -0.642157 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 54% (540/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -5.002857, rho = -0.485260 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 60% (60/100) (classification) Accuracy = 61.4% (614/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.700566, rho = -0.259573 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 85% (85/100) (classification) Accuracy = 93.5% (935/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -8.670170, rho = -0.093942 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.820244 obj = -10.972033, rho = -0.031003 nSV = 84, nBSV = 80 Total nSV = 84 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.725395 obj = -13.739080, rho = -0.064363 nSV = 74, nBSV = 71 Total nSV = 74 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 62 nu = 0.633611 obj = -17.006662, rho = -0.033203 nSV = 65, nBSV = 59 Total nSV = 65 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 41 nu = 0.555815 obj = -20.957052, rho = -0.067559 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 43 nu = 0.480289 obj = -25.404861, rho = -0.033760 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 42 nu = 0.403489 obj = -30.513705, rho = 0.076147 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.335741 obj = -36.784304, rho = 0.027804 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 57 nu = 0.289430 obj = -44.373710, rho = 0.011523 nSV = 30, nBSV = 26 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 50 nu = 0.239168 obj = -52.486973, rho = 0.037833 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 76 nu = 0.198310 obj = -62.150357, rho = 0.093444 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 90 nu = 0.163566 obj = -73.087115, rho = 0.134212 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 78 nu = 0.134537 obj = -86.010654, rho = -0.002090 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -0.908505, rho = -0.915134 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.286975, rho = -0.877925 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.810149, rho = -0.824401 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -2.518766, rho = -0.747410 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -3.447154, rho = -0.636661 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 62% (62/100) (classification) Accuracy = 55.4% (554/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.594462, rho = -0.477356 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 89% (89/100) (classification) Accuracy = 81.8% (818/1000) (classification) * optimization finished, #iter = 46 nu = 0.901204 obj = -5.878065, rho = -0.366041 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 94% (94/100) (classification) Accuracy = 93.7% (937/1000) (classification) * optimization finished, #iter = 40 nu = 0.793901 obj = -7.394270, rho = -0.377272 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 94% (94/100) (classification) Accuracy = 94.3% (943/1000) (classification) * optimization finished, #iter = 36 nu = 0.700836 obj = -9.270662, rho = -0.399017 nSV = 72, nBSV = 70 Total nSV = 72 Accuracy = 95% (95/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 37 nu = 0.616671 obj = -11.512436, rho = -0.363753 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 97% (97/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 41 nu = 0.548713 obj = -13.992746, rho = -0.324167 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 54 nu = 0.459126 obj = -16.904493, rho = -0.259410 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.389611 obj = -20.312897, rho = -0.333839 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 50 nu = 0.317518 obj = -24.505047, rho = -0.352847 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) *..* optimization finished, #iter = 209 nu = 0.268463 obj = -29.636172, rho = -0.289540 nSV = 32, nBSV = 22 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 74 nu = 0.225275 obj = -36.035778, rho = -0.219425 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 54 nu = 0.190370 obj = -43.803454, rho = -0.172247 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.166057 obj = -52.805046, rho = -0.134617 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 75 nu = 0.135957 obj = -63.200853, rho = -0.206636 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.111401 obj = -76.189255, rho = -0.252563 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -0.915633, rho = -0.922207 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.7% (487/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.301727, rho = -0.888057 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 48.7% (487/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.840672, rho = -0.838976 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 48.7% (487/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.581923, rho = -0.768375 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 48.7% (487/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.577834, rho = -0.666820 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -4.864858, rho = -0.520737 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 72% (72/100) (classification) Accuracy = 69.6% (696/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -6.415026, rho = -0.310604 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 92% (92/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 46 nu = 0.873944 obj = -8.187349, rho = -0.217850 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.778333 obj = -10.266341, rho = -0.185551 nSV = 80, nBSV = 76 Total nSV = 80 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 36 nu = 0.685620 obj = -12.785867, rho = -0.149603 nSV = 70, nBSV = 68 Total nSV = 70 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 57 nu = 0.593441 obj = -15.767068, rho = -0.128467 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 38 nu = 0.514560 obj = -19.444622, rho = -0.087827 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 52 nu = 0.442560 obj = -23.754703, rho = -0.077962 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 63 nu = 0.383009 obj = -28.616835, rho = -0.043197 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 97% (97/100) (classification) Accuracy = 99.1% (991/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.311565 obj = -34.445521, rho = -0.075147 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 97% (97/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 70 nu = 0.257916 obj = -42.201526, rho = -0.074545 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 41 nu = 0.217055 obj = -51.998937, rho = -0.160740 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 49 nu = 0.190537 obj = -64.645146, rho = -0.056039 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 99% (990/1000) (classification) .* optimization finished, #iter = 199 nu = 0.167158 obj = -78.786433, rho = -0.103113 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 165 nu = 0.136602 obj = -96.690795, rho = -0.092781 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -0.951128, rho = 0.862482 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -1.349941, rho = 0.802188 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -1.904145, rho = 0.715457 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -2.661057, rho = 0.590700 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -3.666488, rho = 0.411242 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.940287, rho = 0.153101 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 82% (82/100) (classification) Accuracy = 80.1% (801/1000) (classification) * optimization finished, #iter = 49 nu = 0.975475 obj = -6.415958, rho = -0.198201 nSV = 98, nBSV = 96 Total nSV = 98 Accuracy = 93% (93/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 46 nu = 0.867212 obj = -8.113447, rho = -0.221286 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 94% (94/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 42 nu = 0.774278 obj = -10.195696, rho = -0.201015 nSV = 78, nBSV = 76 Total nSV = 78 Accuracy = 94% (94/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 45 nu = 0.688494 obj = -12.654991, rho = -0.124763 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 38 nu = 0.600000 obj = -15.412402, rho = -0.090777 nSV = 62, nBSV = 59 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.505791 obj = -18.572245, rho = -0.072711 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 46 nu = 0.422640 obj = -22.364879, rho = -0.058694 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 27 nu = 0.349298 obj = -27.115114, rho = -0.094310 nSV = 37, nBSV = 33 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 34 nu = 0.299106 obj = -32.818314, rho = -0.048332 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 46 nu = 0.257590 obj = -39.480862, rho = 0.021349 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.208442 obj = -47.157402, rho = 0.007902 nSV = 27, nBSV = 17 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 53 nu = 0.175323 obj = -57.158313, rho = 0.115336 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.148595 obj = -68.225375, rho = 0.272997 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 70 nu = 0.120738 obj = -81.812410, rho = 0.333315 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -0.936552, rho = 0.887596 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.332396, rho = 0.838313 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.885986, rho = 0.767421 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -2.649583, rho = 0.665447 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.680289, rho = 0.518763 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -5.022847, rho = 0.307764 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 68% (68/100) (classification) Accuracy = 66.9% (669/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -6.664246, rho = 0.004254 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 95% (95/100) (classification) Accuracy = 93.7% (937/1000) (classification) * optimization finished, #iter = 45 nu = 0.890977 obj = -8.574703, rho = -0.035475 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 97% (97/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 41 nu = 0.820000 obj = -10.813830, rho = -0.023580 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 43 nu = 0.721804 obj = -13.366517, rho = 0.001972 nSV = 74, nBSV = 70 Total nSV = 74 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 45 nu = 0.639021 obj = -16.294640, rho = 0.040611 nSV = 66, nBSV = 61 Total nSV = 66 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 57 nu = 0.527359 obj = -19.691750, rho = 0.011687 nSV = 57, nBSV = 50 Total nSV = 57 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 54 nu = 0.452288 obj = -23.735186, rho = -0.000398 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 43 nu = 0.379348 obj = -28.409688, rho = 0.022010 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 90 nu = 0.312994 obj = -33.949141, rho = 0.019075 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.260557 obj = -40.570545, rho = -0.023237 nSV = 32, nBSV = 22 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 48 nu = 0.213211 obj = -49.068758, rho = -0.030309 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 55 nu = 0.182195 obj = -59.396177, rho = -0.018693 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.152500 obj = -72.389646, rho = -0.091959 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .**.* optimization finished, #iter = 244 nu = 0.128427 obj = -87.462564, rho = -0.141857 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.947640, rho = -0.895386 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.342724, rho = -0.849518 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.889213, rho = -0.783540 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.630162, rho = -0.688633 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.602561, rho = -0.552114 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 60% (60/100) (classification) Accuracy = 56.1% (561/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.808014, rho = -0.355738 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 86% (86/100) (classification) Accuracy = 86.8% (868/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.202274, rho = -0.221389 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 46 nu = 0.842411 obj = -7.840816, rho = -0.177509 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 40 nu = 0.759562 obj = -9.765987, rho = -0.082100 nSV = 76, nBSV = 74 Total nSV = 76 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 45 nu = 0.663548 obj = -11.937940, rho = -0.002087 nSV = 70, nBSV = 65 Total nSV = 70 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 46 nu = 0.560000 obj = -14.454393, rho = -0.032231 nSV = 58, nBSV = 54 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 56 nu = 0.484807 obj = -17.318549, rho = -0.113926 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 33 nu = 0.404478 obj = -20.563268, rho = -0.053014 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 38 nu = 0.330622 obj = -24.250621, rho = -0.013268 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 68 nu = 0.271307 obj = -28.458587, rho = 0.025069 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.225141 obj = -33.287042, rho = 0.089103 nSV = 27, nBSV = 17 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 43 nu = 0.179292 obj = -39.174391, rho = 0.008648 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 48 nu = 0.150527 obj = -46.111240, rho = -0.126139 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 92 nu = 0.119449 obj = -54.057554, rho = -0.145275 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 67 nu = 0.100263 obj = -64.039486, rho = -0.039883 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.951108, rho = 0.878604 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.349899, rho = 0.825379 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.904058, rho = 0.748816 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.660878, rho = 0.638684 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.666118, rho = 0.480265 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 52% (52/100) (classification) Accuracy = 52.5% (525/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.939522, rho = 0.252388 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 82% (82/100) (classification) Accuracy = 83.5% (835/1000) (classification) * optimization finished, #iter = 49 nu = 0.963311 obj = -6.417420, rho = -0.020977 nSV = 98, nBSV = 96 Total nSV = 98 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 49 nu = 0.876975 obj = -8.082845, rho = -0.034364 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 40 nu = 0.780520 obj = -10.017370, rho = -0.047748 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 40 nu = 0.677568 obj = -12.265088, rho = -0.068061 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 35 nu = 0.573479 obj = -14.893945, rho = -0.069354 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 42 nu = 0.492801 obj = -17.986039, rho = -0.103276 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 59 nu = 0.404504 obj = -21.781474, rho = -0.126719 nSV = 46, nBSV = 38 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 35 nu = 0.344427 obj = -26.364478, rho = -0.128870 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 37 nu = 0.294528 obj = -31.714196, rho = -0.060440 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 57 nu = 0.247920 obj = -37.686176, rho = -0.139902 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.208129 obj = -44.384955, rho = -0.080981 nSV = 22, nBSV = 18 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 48 nu = 0.173201 obj = -51.355968, rho = 0.006753 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 89 nu = 0.136360 obj = -58.584127, rho = 0.015455 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 74 nu = 0.115710 obj = -66.193899, rho = -0.139974 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 45 nu = 0.840000 obj = -0.822063, rho = 0.921161 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 51.8% (518/1000) (classification) * optimization finished, #iter = 45 nu = 0.840000 obj = -1.171183, rho = 0.886595 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 51.8% (518/1000) (classification) * optimization finished, #iter = 44 nu = 0.840000 obj = -1.661280, rho = 0.836872 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 51.8% (518/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -2.341234, rho = 0.765349 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 51.8% (518/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -3.267531, rho = 0.662466 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 51.8% (518/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -4.492817, rho = 0.514929 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 59% (59/100) (classification) Accuracy = 52.5% (525/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -6.033630, rho = 0.302249 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 79% (79/100) (classification) Accuracy = 73% (730/1000) (classification) * optimization finished, #iter = 43 nu = 0.820569 obj = -7.799975, rho = 0.083431 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 93% (93/100) (classification) Accuracy = 91.8% (918/1000) (classification) * optimization finished, #iter = 39 nu = 0.760000 obj = -9.705318, rho = 0.049167 nSV = 77, nBSV = 75 Total nSV = 77 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 41 nu = 0.673299 obj = -11.738314, rho = 0.036218 nSV = 70, nBSV = 65 Total nSV = 70 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 41 nu = 0.560254 obj = -13.972838, rho = 0.033274 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 33 nu = 0.469407 obj = -16.623427, rho = 0.051973 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 118 nu = 0.388800 obj = -19.517985, rho = 0.000002 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 90 nu = 0.310562 obj = -23.112665, rho = -0.005439 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 93 nu = 0.262511 obj = -27.444393, rho = -0.002972 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 138 nu = 0.210744 obj = -32.500175, rho = 0.005364 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.174329 obj = -38.943419, rho = 0.083773 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.149139 obj = -46.740767, rho = 0.334610 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.125490 obj = -54.001594, rho = 0.415480 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 123 nu = 0.098248 obj = -62.748468, rho = 0.454458 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -0.844175, rho = -0.959209 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -1.204322, rho = -0.941324 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -1.711706, rho = -0.915598 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -2.419472, rho = -0.878591 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -3.391873, rho = -0.825360 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -4.696094, rho = -0.748789 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51.1% (511/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -6.376557, rho = -0.638645 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 65% (65/100) (classification) Accuracy = 59.2% (592/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -8.389109, rho = -0.480209 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 85% (85/100) (classification) Accuracy = 88.6% (886/1000) (classification) * optimization finished, #iter = 44 nu = 0.800000 obj = -10.640803, rho = -0.337306 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 95% (95/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 44 nu = 0.726728 obj = -13.130775, rho = -0.226826 nSV = 74, nBSV = 72 Total nSV = 74 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 52 nu = 0.623821 obj = -15.881745, rho = -0.145845 nSV = 66, nBSV = 59 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 44 nu = 0.523121 obj = -19.051561, rho = -0.119951 nSV = 56, nBSV = 48 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 70 nu = 0.436226 obj = -22.877514, rho = -0.078588 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.364031 obj = -27.542476, rho = -0.039894 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 34 nu = 0.305799 obj = -32.813183, rho = -0.090538 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 64 nu = 0.252707 obj = -39.261326, rho = -0.058590 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.212573 obj = -46.935468, rho = -0.051209 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.171397 obj = -56.121124, rho = -0.063063 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 76 nu = 0.146583 obj = -67.440955, rho = -0.102768 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 99 nu = 0.123197 obj = -79.584427, rho = -0.334797 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -0.949629, rho = 0.868396 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 52.5% (525/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.346839, rho = 0.810695 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 52.5% (525/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.897728, rho = 0.727694 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 52.5% (525/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -2.647779, rho = 0.608302 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 52.5% (525/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -3.639014, rho = 0.436562 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 56% (56/100) (classification) Accuracy = 54.6% (546/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -4.883440, rho = 0.189522 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 85% (85/100) (classification) Accuracy = 86% (860/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.335233, rho = -0.034594 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 94% (94/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 46 nu = 0.864397 obj = -8.032438, rho = -0.010408 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 95% (95/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 40 nu = 0.760000 obj = -10.086280, rho = 0.044315 nSV = 77, nBSV = 75 Total nSV = 77 Accuracy = 96% (96/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 40 nu = 0.673148 obj = -12.578690, rho = 0.045452 nSV = 69, nBSV = 66 Total nSV = 69 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 36 nu = 0.587140 obj = -15.477269, rho = 0.000804 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 44 nu = 0.508217 obj = -18.806301, rho = 0.026262 nSV = 54, nBSV = 47 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 41 nu = 0.421986 obj = -22.914420, rho = 0.033475 nSV = 45, nBSV = 41 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 51 nu = 0.357722 obj = -27.881814, rho = 0.000370 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 71 nu = 0.317898 obj = -33.553720, rho = -0.082893 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 92 nu = 0.263497 obj = -39.497666, rho = -0.113962 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 99.3% (993/1000) (classification) .*.....* optimization finished, #iter = 697 nu = 0.208442 obj = -46.746625, rho = -0.114686 nSV = 27, nBSV = 16 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) .*.* optimization finished, #iter = 232 nu = 0.171026 obj = -56.648762, rho = -0.064427 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) .**.* optimization finished, #iter = 163 nu = 0.151445 obj = -67.262053, rho = 0.005784 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*..* optimization finished, #iter = 390 nu = 0.121094 obj = -79.451740, rho = 0.026760 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -0.913529, rho = -0.912315 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.297371, rho = -0.873869 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.831658, rho = -0.818567 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -2.563272, rho = -0.739018 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -3.539242, rho = -0.624590 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.785006, rho = -0.459992 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 70% (70/100) (classification) Accuracy = 62.2% (622/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.249801, rho = -0.223225 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 94% (94/100) (classification) Accuracy = 92.1% (921/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -7.827456, rho = -0.136751 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 97% (97/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 42 nu = 0.755029 obj = -9.661119, rho = -0.134966 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 41 nu = 0.656321 obj = -11.824890, rho = -0.102499 nSV = 67, nBSV = 64 Total nSV = 67 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 35 nu = 0.568511 obj = -14.278193, rho = -0.074496 nSV = 58, nBSV = 56 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 44 nu = 0.473317 obj = -17.013858, rho = -0.089730 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 40 nu = 0.393157 obj = -20.228235, rho = -0.091104 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.322528 obj = -23.998162, rho = -0.113665 nSV = 36, nBSV = 27 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.265880 obj = -28.629006, rho = -0.105747 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 56 nu = 0.220399 obj = -34.333306, rho = -0.178093 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 66 nu = 0.182708 obj = -41.527547, rho = -0.225130 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 37 nu = 0.152112 obj = -50.646870, rho = -0.267183 nSV = 18, nBSV = 14 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 48 nu = 0.129590 obj = -61.535898, rho = -0.324053 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 85 nu = 0.106504 obj = -75.320762, rho = -0.306961 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 50 nu = 0.920000 obj = -0.895937, rho = 0.891753 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 53.2% (532/1000) (classification) * optimization finished, #iter = 50 nu = 0.920000 obj = -1.273584, rho = 0.844293 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 53.2% (532/1000) (classification) * optimization finished, #iter = 50 nu = 0.920000 obj = -1.800586, rho = 0.776023 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 53.2% (532/1000) (classification) * optimization finished, #iter = 50 nu = 0.920000 obj = -2.525078, rho = 0.677820 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 53.2% (532/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -3.497756, rho = 0.536560 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 53.2% (532/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -4.753170, rho = 0.333365 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 63% (63/100) (classification) Accuracy = 64.3% (643/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -6.261610, rho = 0.041079 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 85% (85/100) (classification) Accuracy = 88.1% (881/1000) (classification) * optimization finished, #iter = 54 nu = 0.874359 obj = -7.870381, rho = -0.145078 nSV = 89, nBSV = 85 Total nSV = 89 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 47 nu = 0.774152 obj = -9.652452, rho = -0.122320 nSV = 79, nBSV = 75 Total nSV = 79 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 47 nu = 0.657341 obj = -11.658249, rho = -0.144617 nSV = 68, nBSV = 64 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.552738 obj = -14.009290, rho = -0.143940 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 34 nu = 0.466192 obj = -16.845417, rho = -0.107807 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 38 nu = 0.389268 obj = -20.061268, rho = -0.126570 nSV = 41, nBSV = 38 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 35 nu = 0.331218 obj = -23.694549, rho = -0.086859 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.268852 obj = -27.400929, rho = -0.044424 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 160 nu = 0.216489 obj = -31.628017, rho = -0.049353 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 60 nu = 0.173591 obj = -36.751078, rho = -0.084191 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 95 nu = 0.141030 obj = -42.613089, rho = -0.154796 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 71 nu = 0.112941 obj = -49.341214, rho = -0.223723 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *....*.* optimization finished, #iter = 571 nu = 0.090184 obj = -57.501067, rho = -0.235833 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -0.936654, rho = -0.927193 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.332606, rho = -0.895270 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.886421, rho = -0.849352 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.650484, rho = -0.783300 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.682153, rho = -0.688288 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -5.026705, rho = -0.551618 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 63% (63/100) (classification) Accuracy = 63.2% (632/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -6.672229, rho = -0.355024 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 90% (90/100) (classification) Accuracy = 92.3% (923/1000) (classification) * optimization finished, #iter = 49 nu = 0.916926 obj = -8.530428, rho = -0.211912 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 96% (96/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 44 nu = 0.832309 obj = -10.607726, rho = -0.165649 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 54 nu = 0.714502 obj = -12.947963, rho = -0.165069 nSV = 74, nBSV = 70 Total nSV = 74 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 41 nu = 0.608747 obj = -15.729225, rho = -0.147624 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 54 nu = 0.512671 obj = -19.095652, rho = -0.204068 nSV = 54, nBSV = 45 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 76 nu = 0.425843 obj = -23.296910, rho = -0.242423 nSV = 49, nBSV = 40 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 36 nu = 0.363405 obj = -28.578035, rho = -0.281061 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 87 nu = 0.307080 obj = -35.181832, rho = -0.283919 nSV = 37, nBSV = 28 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 62 nu = 0.267579 obj = -43.327825, rho = -0.249363 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 72 nu = 0.234849 obj = -52.405817, rho = -0.166836 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 69 nu = 0.198089 obj = -62.274325, rho = -0.098731 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 77 nu = 0.169484 obj = -72.506093, rho = -0.047265 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 143 nu = 0.131875 obj = -83.579042, rho = -0.051326 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.952643, rho = 0.878817 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.353075, rho = 0.825684 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.910631, rho = 0.749255 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.674477, rho = 0.639316 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.694256, rho = 0.481174 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.1% (511/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.997743, rho = 0.253694 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 71% (71/100) (classification) Accuracy = 74.9% (749/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -6.534620, rho = -0.073523 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 45 nu = 0.885806 obj = -8.269824, rho = -0.096369 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.808440 obj = -10.267642, rho = -0.143165 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 49 nu = 0.701623 obj = -12.430874, rho = -0.088647 nSV = 73, nBSV = 68 Total nSV = 73 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 59 nu = 0.593220 obj = -14.978127, rho = -0.127278 nSV = 62, nBSV = 55 Total nSV = 62 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 44 nu = 0.500265 obj = -17.877722, rho = -0.165797 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 63 nu = 0.418235 obj = -21.014649, rho = -0.163109 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 37 nu = 0.343888 obj = -24.586392, rho = -0.164016 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 64 nu = 0.281030 obj = -28.397090, rho = -0.172307 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 70 nu = 0.228762 obj = -32.805915, rho = -0.147139 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 48 nu = 0.184914 obj = -37.695509, rho = -0.135358 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.144374 obj = -42.835403, rho = -0.121483 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.116733 obj = -49.000536, rho = -0.198771 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.097428 obj = -54.009433, rho = -0.313861 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 42 nu = 0.680000 obj = -0.671039, rho = 0.963068 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 66% (66/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 42 nu = 0.680000 obj = -0.959604, rho = 0.946875 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 66% (66/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 44 nu = 0.680000 obj = -1.368650, rho = 0.923555 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 66% (66/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 47 nu = 0.680000 obj = -1.944542, rho = 0.889623 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 66% (66/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 46 nu = 0.680000 obj = -2.747064, rho = 0.841229 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 66% (66/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 46 nu = 0.680000 obj = -3.847928, rho = 0.771615 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 66% (66/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 50 nu = 0.680000 obj = -5.320727, rho = 0.671471 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 66% (66/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 42 nu = 0.680000 obj = -7.210117, rho = 0.527428 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 77% (77/100) (classification) Accuracy = 56.6% (566/1000) (classification) * optimization finished, #iter = 40 nu = 0.680000 obj = -9.453780, rho = 0.320218 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 87% (87/100) (classification) Accuracy = 85.2% (852/1000) (classification) * optimization finished, #iter = 39 nu = 0.620000 obj = -12.030260, rho = 0.279884 nSV = 63, nBSV = 60 Total nSV = 63 Accuracy = 92% (92/100) (classification) Accuracy = 92% (920/1000) (classification) * optimization finished, #iter = 33 nu = 0.556950 obj = -15.077382, rho = 0.255216 nSV = 56, nBSV = 54 Total nSV = 56 Accuracy = 96% (96/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 44 nu = 0.487613 obj = -18.607630, rho = 0.270016 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 97% (97/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 44 nu = 0.422523 obj = -22.700791, rho = 0.244330 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 91 nu = 0.359779 obj = -27.596030, rho = 0.186020 nSV = 41, nBSV = 32 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 51 nu = 0.309339 obj = -33.352393, rho = 0.168777 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 50 nu = 0.256355 obj = -39.983908, rho = 0.086196 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 56 nu = 0.218760 obj = -47.596421, rho = 0.314451 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 132 nu = 0.174331 obj = -56.613862, rho = 0.283533 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.*.* optimization finished, #iter = 148 nu = 0.148993 obj = -67.525891, rho = 0.477125 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.118994 obj = -80.662087, rho = 0.457924 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -0.915876, rho = 0.884817 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 51.8% (518/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -1.302228, rho = 0.834315 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 51.8% (518/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -1.841709, rho = 0.761671 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 51.8% (518/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -2.584068, rho = 0.657176 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 51.8% (518/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.582273, rho = 0.506865 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 51.9% (519/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.874042, rho = 0.290650 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 66% (66/100) (classification) Accuracy = 68.5% (685/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.434030, rho = -0.020365 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 94% (94/100) (classification) Accuracy = 93.6% (936/1000) (classification) * optimization finished, #iter = 48 nu = 0.876059 obj = -8.197062, rho = -0.118737 nSV = 90, nBSV = 86 Total nSV = 90 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 47 nu = 0.782778 obj = -10.251397, rho = -0.170662 nSV = 80, nBSV = 76 Total nSV = 80 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 39 nu = 0.679169 obj = -12.716691, rho = -0.170935 nSV = 69, nBSV = 66 Total nSV = 69 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 39 nu = 0.614235 obj = -15.625810, rho = -0.207711 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 42 nu = 0.516909 obj = -18.725100, rho = -0.204752 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 38 nu = 0.435851 obj = -22.281540, rho = -0.232381 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 78 nu = 0.363408 obj = -26.052921, rho = -0.306820 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 37 nu = 0.294225 obj = -30.483784, rho = -0.253821 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 51 nu = 0.243998 obj = -35.278193, rho = -0.286203 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 72 nu = 0.195124 obj = -40.260685, rho = -0.333342 nSV = 25, nBSV = 15 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 80 nu = 0.155346 obj = -46.346670, rho = -0.332923 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 75 nu = 0.125868 obj = -52.983347, rho = -0.393949 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.101302 obj = -59.591854, rho = -0.466341 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 42 nu = 0.800000 obj = -0.784100, rho = -0.965052 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -1.117862, rho = -0.949973 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -1.587241, rho = -0.928038 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -2.240235, rho = -0.896487 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -3.133636, rho = -0.851101 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -4.323777, rho = -0.785816 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 42 nu = 0.800000 obj = -5.839229, rho = -0.691560 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 76% (76/100) (classification) Accuracy = 66.1% (661/1000) (classification) * optimization finished, #iter = 42 nu = 0.800000 obj = -7.612524, rho = -0.556324 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 93% (93/100) (classification) Accuracy = 89.6% (896/1000) (classification) * optimization finished, #iter = 37 nu = 0.720000 obj = -9.637312, rho = -0.522944 nSV = 73, nBSV = 71 Total nSV = 73 Accuracy = 96% (96/100) (classification) Accuracy = 93.1% (931/1000) (classification) * optimization finished, #iter = 38 nu = 0.640000 obj = -12.035471, rho = -0.531049 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 96% (96/100) (classification) Accuracy = 94.5% (945/1000) (classification) * optimization finished, #iter = 42 nu = 0.562473 obj = -14.797505, rho = -0.464383 nSV = 58, nBSV = 54 Total nSV = 58 Accuracy = 97% (97/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 32 nu = 0.482710 obj = -18.030469, rho = -0.442436 nSV = 50, nBSV = 47 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 34 nu = 0.421712 obj = -21.759519, rho = -0.381967 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 67 nu = 0.348805 obj = -25.731185, rho = -0.420608 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 73 nu = 0.295011 obj = -30.021457, rho = -0.465200 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 85 nu = 0.234225 obj = -34.661621, rho = -0.455553 nSV = 30, nBSV = 19 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 53 nu = 0.189564 obj = -40.391380, rho = -0.462128 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 120 nu = 0.152102 obj = -46.903994, rho = -0.492929 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *..* optimization finished, #iter = 210 nu = 0.126036 obj = -54.468366, rho = -0.509749 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 168 nu = 0.101168 obj = -62.961037, rho = -0.463013 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -0.840295, rho = 0.924726 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 47 nu = 0.860000 obj = -1.196296, rho = 0.891970 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 48 nu = 0.860000 obj = -1.695099, rho = 0.845191 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 48 nu = 0.860000 obj = -2.385110, rho = 0.777315 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 48 nu = 0.860000 obj = -3.320774, rho = 0.679679 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 48 nu = 0.860000 obj = -4.548982, rho = 0.539918 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 48 nu = 0.860000 obj = -6.072161, rho = 0.338200 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 81% (81/100) (classification) Accuracy = 71.2% (712/1000) (classification) * optimization finished, #iter = 46 nu = 0.859817 obj = -7.759272, rho = 0.047148 nSV = 87, nBSV = 83 Total nSV = 87 Accuracy = 98% (98/100) (classification) Accuracy = 94.6% (946/1000) (classification) * optimization finished, #iter = 54 nu = 0.756241 obj = -9.510740, rho = -0.029320 nSV = 78, nBSV = 72 Total nSV = 78 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 44 nu = 0.653201 obj = -11.522311, rho = -0.038583 nSV = 68, nBSV = 62 Total nSV = 68 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 60 nu = 0.547092 obj = -13.869372, rho = -0.017927 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 51 nu = 0.462174 obj = -16.653118, rho = 0.019464 nSV = 48, nBSV = 44 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 51 nu = 0.383384 obj = -19.749240, rho = 0.040061 nSV = 44, nBSV = 36 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 66 nu = 0.324293 obj = -23.315580, rho = 0.016367 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.271291 obj = -26.805516, rho = -0.089520 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *..* optimization finished, #iter = 261 nu = 0.214483 obj = -30.519929, rho = -0.061102 nSV = 27, nBSV = 16 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.167278 obj = -35.151042, rho = -0.010143 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.134573 obj = -40.820996, rho = 0.049782 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 58 nu = 0.109956 obj = -46.861068, rho = 0.065576 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 36 nu = 0.087991 obj = -53.298004, rho = -0.007244 nSV = 11, nBSV = 6 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -0.842498, rho = 0.919197 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -1.200853, rho = 0.883769 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -1.704527, rho = 0.832808 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -2.404619, rho = 0.759502 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -3.361139, rho = 0.654056 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -4.632501, rho = 0.502377 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 58% (58/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -6.244973, rho = 0.284194 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 79% (79/100) (classification) Accuracy = 73.6% (736/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -8.125645, rho = 0.021728 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 95% (95/100) (classification) Accuracy = 94% (940/1000) (classification) * optimization finished, #iter = 40 nu = 0.797045 obj = -10.179829, rho = -0.040500 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 53 nu = 0.684808 obj = -12.424410, rho = -0.052487 nSV = 72, nBSV = 65 Total nSV = 72 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.586727 obj = -15.114793, rho = -0.011309 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 52 nu = 0.491602 obj = -18.323236, rho = -0.045971 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 46 nu = 0.416399 obj = -22.333300, rho = -0.065304 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 33 nu = 0.352108 obj = -27.079318, rho = -0.154542 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 38 nu = 0.299280 obj = -32.745623, rho = -0.123275 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 80 nu = 0.252701 obj = -39.191514, rho = -0.104724 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 92 nu = 0.213833 obj = -46.552544, rho = -0.148782 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 85 nu = 0.178094 obj = -54.844507, rho = -0.198934 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 148 nu = 0.143651 obj = -64.515172, rho = -0.132672 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 172 nu = 0.119141 obj = -75.360348, rho = -0.103181 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.947158, rho = 0.840980 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.4% (494/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.341726, rho = 0.771258 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.4% (494/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.887146, rho = 0.670966 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.4% (494/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.625885, rho = 0.526701 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.4% (494/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.593712, rho = 0.319183 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 64% (64/100) (classification) Accuracy = 58.4% (584/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.789704, rho = 0.020678 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 84% (84/100) (classification) Accuracy = 83.8% (838/1000) (classification) * optimization finished, #iter = 48 nu = 0.935518 obj = -6.164130, rho = -0.198718 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 94% (94/100) (classification) Accuracy = 92.3% (923/1000) (classification) * optimization finished, #iter = 49 nu = 0.838924 obj = -7.779646, rho = -0.146551 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 96% (96/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 43 nu = 0.752421 obj = -9.679066, rho = -0.073848 nSV = 76, nBSV = 72 Total nSV = 76 Accuracy = 96% (96/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 49 nu = 0.657428 obj = -11.890725, rho = -0.076855 nSV = 67, nBSV = 63 Total nSV = 67 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 38 nu = 0.572344 obj = -14.378047, rho = -0.136948 nSV = 59, nBSV = 56 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.477747 obj = -17.125916, rho = -0.176866 nSV = 50, nBSV = 43 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 52 nu = 0.394755 obj = -20.494998, rho = -0.178477 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 74 nu = 0.327176 obj = -24.462406, rho = -0.159715 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 51 nu = 0.270362 obj = -29.285735, rho = -0.139774 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 99 nu = 0.220569 obj = -35.318357, rho = -0.162847 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 57 nu = 0.196964 obj = -42.542592, rho = -0.079886 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 59 nu = 0.157091 obj = -50.345990, rho = -0.054187 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 56 nu = 0.137744 obj = -59.384284, rho = -0.051665 nSV = 16, nBSV = 11 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.109959 obj = -67.624619, rho = -0.079214 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -0.895638, rho = 0.900215 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -1.272966, rho = 0.856464 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -1.799307, rho = 0.793531 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -2.522432, rho = 0.703005 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -3.492283, rho = 0.572787 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -4.741844, rho = 0.385476 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 68% (68/100) (classification) Accuracy = 62% (620/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -6.238176, rho = 0.116038 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 90% (90/100) (classification) Accuracy = 88.4% (884/1000) (classification) * optimization finished, #iter = 54 nu = 0.858386 obj = -7.863581, rho = -0.040710 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 96% (96/100) (classification) Accuracy = 93.9% (939/1000) (classification) * optimization finished, #iter = 43 nu = 0.767435 obj = -9.724638, rho = 0.024886 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 99% (99/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 39 nu = 0.660641 obj = -11.852010, rho = -0.030512 nSV = 68, nBSV = 65 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 51 nu = 0.568217 obj = -14.285742, rho = -0.019746 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 50 nu = 0.470061 obj = -17.048739, rho = 0.043461 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 48 nu = 0.394479 obj = -20.357842, rho = 0.009878 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 42 nu = 0.334628 obj = -24.019747, rho = 0.030433 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 92 nu = 0.271524 obj = -28.048230, rho = 0.109004 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 47 nu = 0.221227 obj = -32.420758, rho = 0.135059 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 52 nu = 0.179925 obj = -37.419734, rho = 0.122649 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.150980 obj = -42.341878, rho = 0.072094 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.117877 obj = -46.903229, rho = 0.093555 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*..* optimization finished, #iter = 307 nu = 0.088908 obj = -51.575638, rho = 0.014245 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -0.916388, rho = -0.926615 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.303286, rho = -0.894439 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.843898, rho = -0.848156 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -2.588597, rho = -0.781580 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -3.591644, rho = -0.685814 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.893432, rho = -0.548059 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 68% (68/100) (classification) Accuracy = 65.4% (654/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.474149, rho = -0.349905 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 93% (93/100) (classification) Accuracy = 92.4% (924/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -8.303050, rho = -0.281062 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 100% (100/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 46 nu = 0.820000 obj = -10.326171, rho = -0.228299 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 51 nu = 0.695042 obj = -12.602401, rho = -0.240972 nSV = 72, nBSV = 67 Total nSV = 72 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 47 nu = 0.592930 obj = -15.278209, rho = -0.181647 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 37 nu = 0.510725 obj = -18.440579, rho = -0.187423 nSV = 52, nBSV = 49 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.424198 obj = -21.890646, rho = -0.204104 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 33 nu = 0.351393 obj = -26.175847, rho = -0.164925 nSV = 36, nBSV = 32 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 59 nu = 0.296051 obj = -30.995112, rho = -0.200467 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.238692 obj = -36.717684, rho = -0.226767 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 92 nu = 0.199107 obj = -43.590854, rho = -0.314388 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 61 nu = 0.167699 obj = -51.306319, rho = -0.444448 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 60 nu = 0.134889 obj = -59.964593, rho = -0.512071 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 44 nu = 0.109931 obj = -70.628864, rho = -0.580625 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -0.821042, rho = -0.958492 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -1.169071, rho = -0.940292 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -1.656910, rho = -0.914113 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -2.332193, rho = -0.876456 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -3.248823, rho = -0.822289 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -4.454107, rho = -0.744371 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 61% (61/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -5.953533, rho = -0.632291 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 81% (81/100) (classification) Accuracy = 71% (710/1000) (classification) * optimization finished, #iter = 42 nu = 0.803853 obj = -7.661800, rho = -0.537822 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 91% (91/100) (classification) Accuracy = 88.3% (883/1000) (classification) * optimization finished, #iter = 45 nu = 0.732526 obj = -9.595552, rho = -0.444412 nSV = 75, nBSV = 71 Total nSV = 75 Accuracy = 94% (94/100) (classification) Accuracy = 94% (940/1000) (classification) * optimization finished, #iter = 52 nu = 0.659235 obj = -11.831860, rho = -0.384978 nSV = 68, nBSV = 64 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 41 nu = 0.560968 obj = -14.310875, rho = -0.304857 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 60 nu = 0.471289 obj = -17.200597, rho = -0.298789 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 82 nu = 0.396354 obj = -20.625265, rho = -0.260377 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 46 nu = 0.325898 obj = -24.808514, rho = -0.249704 nSV = 35, nBSV = 30 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 74 nu = 0.269624 obj = -30.011066, rho = -0.254177 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 80 nu = 0.230737 obj = -36.382923, rho = -0.209573 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 52 nu = 0.201964 obj = -43.807560, rho = -0.134970 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 75 nu = 0.165584 obj = -51.740858, rho = -0.093509 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 73 nu = 0.139584 obj = -60.198529, rho = -0.048724 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 129 nu = 0.115052 obj = -69.155791, rho = 0.024292 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -0.914510, rho = 0.882325 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.299400, rho = 0.830731 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.835858, rho = 0.756515 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.571962, rho = 0.649759 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.557224, rho = 0.496196 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -4.822214, rho = 0.275303 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 73% (73/100) (classification) Accuracy = 67.3% (673/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.326789, rho = -0.042440 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 95% (95/100) (classification) Accuracy = 92.7% (927/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -7.944672, rho = -0.117997 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 50 nu = 0.780262 obj = -9.714634, rho = -0.064677 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 36 nu = 0.668139 obj = -11.732378, rho = -0.094309 nSV = 69, nBSV = 66 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 55 nu = 0.560764 obj = -13.941088, rho = -0.052288 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 54 nu = 0.462352 obj = -16.535934, rho = -0.047014 nSV = 51, nBSV = 43 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.386025 obj = -19.609166, rho = -0.033095 nSV = 41, nBSV = 37 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 52 nu = 0.318009 obj = -23.125549, rho = -0.040838 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 77 nu = 0.260379 obj = -27.152211, rho = -0.063174 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 50 nu = 0.212748 obj = -32.193737, rho = -0.081011 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 93 nu = 0.176517 obj = -37.713593, rho = -0.176137 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 87 nu = 0.146581 obj = -43.765150, rho = -0.243590 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 51 nu = 0.119946 obj = -50.216874, rho = -0.254931 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 83 nu = 0.097592 obj = -55.776831, rho = -0.434726 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -0.895874, rho = -0.923954 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.273453, rho = -0.890611 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.800315, rho = -0.842650 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -2.524517, rho = -0.773660 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -3.496597, rho = -0.674421 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -4.750771, rho = -0.531670 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 61% (61/100) (classification) Accuracy = 60.6% (606/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -6.256646, rho = -0.326346 nSV = 93, nBSV = 90 Total nSV = 93 Accuracy = 93% (93/100) (classification) Accuracy = 88% (880/1000) (classification) * optimization finished, #iter = 44 nu = 0.858282 obj = -7.955752, rho = -0.211855 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 99% (99/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 50 nu = 0.774092 obj = -9.837870, rho = -0.109547 nSV = 79, nBSV = 75 Total nSV = 79 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 58 nu = 0.668817 obj = -12.029410, rho = -0.109260 nSV = 69, nBSV = 64 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 90 nu = 0.562591 obj = -14.535167, rho = -0.166785 nSV = 61, nBSV = 54 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 40 nu = 0.480389 obj = -17.523791, rho = -0.193471 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 32 nu = 0.401232 obj = -21.004326, rho = -0.221968 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 31 nu = 0.334001 obj = -25.128129, rho = -0.192623 nSV = 36, nBSV = 32 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 55 nu = 0.281274 obj = -29.848289, rho = -0.296494 nSV = 32, nBSV = 23 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 36 nu = 0.235163 obj = -35.509710, rho = -0.315432 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 130 nu = 0.188126 obj = -42.184707, rho = -0.367074 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.164632 obj = -50.390801, rho = -0.483329 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.131138 obj = -58.716457, rho = -0.506199 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 82 nu = 0.113534 obj = -67.804767, rho = -0.243078 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -0.931975, rho = -0.912690 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.322925, rho = -0.874409 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.866390, rho = -0.819344 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -2.609036, rho = -0.740135 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -3.596393, rho = -0.626197 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -4.849255, rho = -0.462304 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 77% (77/100) (classification) Accuracy = 74.7% (747/1000) (classification) * optimization finished, #iter = 50 nu = 0.953305 obj = -6.305573, rho = -0.239562 nSV = 97, nBSV = 94 Total nSV = 97 Accuracy = 99% (99/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 44 nu = 0.869391 obj = -7.934481, rho = -0.201071 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 65 nu = 0.759043 obj = -9.802682, rho = -0.188541 nSV = 80, nBSV = 74 Total nSV = 80 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 40 nu = 0.662896 obj = -12.042956, rho = -0.150803 nSV = 68, nBSV = 65 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 51 nu = 0.558543 obj = -14.704367, rho = -0.146121 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 42 nu = 0.485510 obj = -17.871841, rho = -0.208760 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 40 nu = 0.402868 obj = -21.566708, rho = -0.230761 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 56 nu = 0.338715 obj = -26.195881, rho = -0.233412 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 91 nu = 0.282852 obj = -32.056828, rho = -0.182571 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 62 nu = 0.240930 obj = -39.745919, rho = -0.174212 nSV = 27, nBSV = 22 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 75 nu = 0.211152 obj = -48.735662, rho = -0.220975 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.178191 obj = -59.711999, rho = -0.104394 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 169 nu = 0.147479 obj = -73.597568, rho = -0.074481 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 76 nu = 0.130415 obj = -91.474484, rho = -0.012305 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -0.933505, rho = -0.917025 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.326090, rho = -0.880645 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.872939, rho = -0.828314 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.622588, rho = -0.753038 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.624434, rho = -0.644757 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 53% (53/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.907275, rho = -0.489001 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 76% (76/100) (classification) Accuracy = 77.6% (776/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.434086, rho = -0.320630 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 98% (98/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 49 nu = 0.888477 obj = -8.171806, rho = -0.239089 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 45 nu = 0.795529 obj = -10.153967, rho = -0.208736 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 43 nu = 0.678303 obj = -12.467719, rho = -0.176494 nSV = 71, nBSV = 64 Total nSV = 71 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 43 nu = 0.589166 obj = -15.342862, rho = -0.136682 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 41 nu = 0.512685 obj = -18.580173, rho = -0.021450 nSV = 53, nBSV = 49 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 50 nu = 0.426446 obj = -22.236566, rho = -0.084063 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 62 nu = 0.352977 obj = -26.608112, rho = -0.138272 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 84 nu = 0.291981 obj = -32.022598, rho = -0.123602 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 44 nu = 0.246768 obj = -38.810684, rho = -0.190132 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 57 nu = 0.207739 obj = -47.114990, rho = -0.350814 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 184 nu = 0.174010 obj = -56.616101, rho = -0.454938 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.153752 obj = -66.582357, rho = -0.580703 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 215 nu = 0.124912 obj = -75.778294, rho = -0.550771 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -0.860544, rho = 0.905474 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -1.225579, rho = 0.864029 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -1.737545, rho = 0.804413 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -2.446837, rho = 0.718658 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -3.410953, rho = 0.595303 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -4.681568, rho = 0.417864 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 59% (59/100) (classification) Accuracy = 56.4% (564/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -6.268819, rho = 0.162626 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 84% (84/100) (classification) Accuracy = 84.3% (843/1000) (classification) * optimization finished, #iter = 45 nu = 0.847467 obj = -8.076371, rho = -0.076895 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 97% (97/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 51 nu = 0.770420 obj = -10.177303, rho = -0.068996 nSV = 79, nBSV = 74 Total nSV = 79 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 46 nu = 0.676607 obj = -12.692524, rho = -0.036303 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 58 nu = 0.592971 obj = -15.622064, rho = -0.004494 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 57 nu = 0.511481 obj = -19.106005, rho = -0.073076 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 50 nu = 0.432110 obj = -23.196351, rho = -0.030250 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 42 nu = 0.371408 obj = -27.982630, rho = 0.062215 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 40 nu = 0.309562 obj = -33.585753, rho = 0.098162 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 70 nu = 0.264220 obj = -39.665218, rho = 0.183028 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 91 nu = 0.212061 obj = -46.608719, rho = 0.197201 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.173576 obj = -55.294733, rho = 0.218379 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 160 nu = 0.144340 obj = -66.300566, rho = 0.387306 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 218 nu = 0.119503 obj = -79.908976, rho = 0.413795 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -0.880604, rho = 0.927319 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -1.254479, rho = 0.895700 nSV = 92, nBSV = 88 Total nSV = 92 Accuracy = 55% (55/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -1.779199, rho = 0.849970 nSV = 92, nBSV = 88 Total nSV = 92 Accuracy = 55% (55/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -2.506926, rho = 0.784189 nSV = 92, nBSV = 88 Total nSV = 92 Accuracy = 55% (55/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -3.497741, rho = 0.689566 nSV = 92, nBSV = 88 Total nSV = 92 Accuracy = 55% (55/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -4.807141, rho = 0.553457 nSV = 92, nBSV = 88 Total nSV = 92 Accuracy = 56% (56/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 53 nu = 0.900000 obj = -6.450980, rho = 0.358046 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 72% (72/100) (classification) Accuracy = 71.8% (718/1000) (classification) * optimization finished, #iter = 49 nu = 0.880000 obj = -8.339203, rho = 0.153634 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 95% (95/100) (classification) Accuracy = 93.3% (933/1000) (classification) * optimization finished, #iter = 51 nu = 0.816638 obj = -10.382754, rho = 0.012761 nSV = 85, nBSV = 80 Total nSV = 85 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.707955 obj = -12.554405, rho = -0.008080 nSV = 73, nBSV = 69 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 41 nu = 0.598274 obj = -15.034900, rho = -0.027910 nSV = 64, nBSV = 57 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 50 nu = 0.500812 obj = -17.940807, rho = -0.048103 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 49 nu = 0.410125 obj = -21.371523, rho = -0.098450 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 38 nu = 0.346591 obj = -25.489015, rho = 0.002152 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 58 nu = 0.291986 obj = -29.924223, rho = 0.044634 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 92 nu = 0.231273 obj = -35.022423, rho = 0.052774 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 99.4% (994/1000) (classification) .* optimization finished, #iter = 147 nu = 0.186530 obj = -41.587146, rho = 0.071833 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 95 nu = 0.152334 obj = -49.932894, rho = 0.100493 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.128741 obj = -60.365282, rho = 0.096253 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 84 nu = 0.113828 obj = -72.080756, rho = 0.085804 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -0.875914, rho = 0.889960 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -1.244768, rho = 0.841713 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -1.759104, rho = 0.772312 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -2.465347, rho = 0.672482 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -3.411708, rho = 0.528882 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -4.629128, rho = 0.322320 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 69% (69/100) (classification) Accuracy = 64.6% (646/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -6.082630, rho = 0.025156 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 95% (95/100) (classification) Accuracy = 90.6% (906/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -7.696427, rho = -0.112075 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 99% (99/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 46 nu = 0.755000 obj = -9.502435, rho = -0.000016 nSV = 77, nBSV = 74 Total nSV = 77 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 45 nu = 0.642139 obj = -11.534031, rho = -0.008746 nSV = 69, nBSV = 62 Total nSV = 69 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 67 nu = 0.556573 obj = -13.799479, rho = 0.054197 nSV = 59, nBSV = 52 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 62 nu = 0.472001 obj = -16.192542, rho = -0.016674 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 56 nu = 0.383826 obj = -18.833162, rho = -0.022509 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.310580 obj = -21.657436, rho = 0.016985 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 42 nu = 0.246344 obj = -24.989085, rho = 0.056890 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.199798 obj = -28.752224, rho = 0.046296 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 81 nu = 0.163318 obj = -32.810611, rho = 0.044515 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *...* optimization finished, #iter = 306 nu = 0.126082 obj = -37.041148, rho = 0.045830 nSV = 19, nBSV = 8 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 98 nu = 0.101301 obj = -42.322700, rho = 0.038439 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 52 nu = 0.082598 obj = -47.097032, rho = 0.103770 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -0.838012, rho = 0.894332 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -1.191570, rho = 0.848001 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -1.685319, rho = 0.781357 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -2.364875, rho = 0.685494 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -3.278904, rho = 0.547598 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -4.462350, rho = 0.348325 nSV = 88, nBSV = 84 Total nSV = 88 Accuracy = 63% (63/100) (classification) Accuracy = 58.2% (582/1000) (classification) * optimization finished, #iter = 47 nu = 0.860000 obj = -5.892915, rho = 0.063278 nSV = 88, nBSV = 84 Total nSV = 88 Accuracy = 90% (90/100) (classification) Accuracy = 82.7% (827/1000) (classification) * optimization finished, #iter = 43 nu = 0.817300 obj = -7.450186, rho = -0.128941 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 99% (99/100) (classification) Accuracy = 93.4% (934/1000) (classification) * optimization finished, #iter = 43 nu = 0.730470 obj = -9.178950, rho = -0.210669 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 45 nu = 0.628589 obj = -11.120233, rho = -0.271903 nSV = 66, nBSV = 61 Total nSV = 66 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 56 nu = 0.536182 obj = -13.268568, rho = -0.275214 nSV = 57, nBSV = 51 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 39 nu = 0.446004 obj = -15.714859, rho = -0.288592 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 24 nu = 0.379917 obj = -18.432934, rho = -0.302702 nSV = 39, nBSV = 36 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 39 nu = 0.315115 obj = -20.972501, rho = -0.301020 nSV = 32, nBSV = 28 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.241524 obj = -23.461346, rho = -0.324585 nSV = 30, nBSV = 20 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 45 nu = 0.188851 obj = -26.569991, rho = -0.330937 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 39 nu = 0.149816 obj = -29.885872, rho = -0.388601 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 39 nu = 0.120684 obj = -33.172757, rho = -0.352031 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.092132 obj = -35.694973, rho = -0.319748 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 172 nu = 0.068173 obj = -38.640375, rho = -0.314649 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.949072, rho = -0.903387 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.345687, rho = -0.861027 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.895343, rho = -0.800094 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.642845, rho = -0.712446 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.628804, rho = -0.586368 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 55% (55/100) (classification) Accuracy = 53.9% (539/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.862315, rho = -0.405011 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 89% (89/100) (classification) Accuracy = 83.3% (833/1000) (classification) * optimization finished, #iter = 52 nu = 0.959546 obj = -6.275694, rho = -0.197353 nSV = 97, nBSV = 94 Total nSV = 97 Accuracy = 96% (96/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 45 nu = 0.856256 obj = -7.897628, rho = -0.236660 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.777093 obj = -9.755431, rho = -0.176790 nSV = 79, nBSV = 75 Total nSV = 79 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 48 nu = 0.661788 obj = -11.825359, rho = -0.107086 nSV = 68, nBSV = 65 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 58 nu = 0.563873 obj = -14.192147, rho = -0.125845 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 56 nu = 0.465177 obj = -17.042327, rho = -0.132154 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 46 nu = 0.395724 obj = -20.408305, rho = -0.147862 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 61 nu = 0.324755 obj = -24.365485, rho = -0.154531 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *..* optimization finished, #iter = 261 nu = 0.265213 obj = -29.370881, rho = -0.073563 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *..* optimization finished, #iter = 295 nu = 0.224140 obj = -35.811733, rho = -0.018205 nSV = 28, nBSV = 18 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.184580 obj = -43.979578, rho = -0.040489 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 32 nu = 0.158357 obj = -54.696108, rho = 0.010384 nSV = 19, nBSV = 14 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 55 nu = 0.142608 obj = -67.250702, rho = 0.143138 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 89 nu = 0.124803 obj = -79.908585, rho = 0.140106 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -0.948820, rho = 0.836814 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -1.345166, rho = 0.765265 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -1.894264, rho = 0.662345 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -2.640613, rho = 0.514300 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -3.624186, rho = 0.301345 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 64% (64/100) (classification) Accuracy = 60.5% (605/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -4.852760, rho = -0.004980 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 92% (92/100) (classification) Accuracy = 92.5% (925/1000) (classification) * optimization finished, #iter = 51 nu = 0.922084 obj = -6.289286, rho = -0.163330 nSV = 94, nBSV = 91 Total nSV = 94 Accuracy = 96% (96/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 46 nu = 0.857419 obj = -8.053002, rho = -0.143545 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.765721 obj = -10.169685, rho = -0.092379 nSV = 79, nBSV = 75 Total nSV = 79 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 42 nu = 0.679085 obj = -12.681719, rho = -0.090064 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 63 nu = 0.583046 obj = -15.746852, rho = -0.071586 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 43 nu = 0.500122 obj = -19.551351, rho = -0.044769 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 37 nu = 0.438410 obj = -24.258333, rho = -0.110830 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 38 nu = 0.374145 obj = -29.984611, rho = -0.146368 nSV = 40, nBSV = 36 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 73 nu = 0.331235 obj = -36.513512, rho = -0.130267 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 65 nu = 0.272719 obj = -44.483488, rho = -0.069663 nSV = 32, nBSV = 23 Total nSV = 32 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.228605 obj = -55.002150, rho = -0.158062 nSV = 28, nBSV = 18 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.194859 obj = -69.087322, rho = -0.216579 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 90 nu = 0.172856 obj = -86.845021, rho = -0.376085 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 115 nu = 0.151192 obj = -108.354176, rho = -0.560117 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.949704, rho = -0.904034 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.1% (511/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.346994, rho = -0.861957 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.1% (511/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.898048, rho = -0.801432 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.1% (511/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.648441, rho = -0.714370 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.1% (511/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.640384, rho = -0.589136 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 53% (53/100) (classification) Accuracy = 53.8% (538/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.886276, rho = -0.408993 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 89% (89/100) (classification) Accuracy = 84.7% (847/1000) (classification) * optimization finished, #iter = 51 nu = 0.966432 obj = -6.306008, rho = -0.201976 nSV = 98, nBSV = 95 Total nSV = 98 Accuracy = 95% (95/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 46 nu = 0.869703 obj = -7.911014, rho = -0.183917 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 41 nu = 0.760000 obj = -9.797212, rho = -0.161671 nSV = 77, nBSV = 75 Total nSV = 77 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 37 nu = 0.661889 obj = -11.989721, rho = -0.097791 nSV = 68, nBSV = 64 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 49 nu = 0.570867 obj = -14.541455, rho = -0.103619 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 36 nu = 0.489081 obj = -17.448009, rho = -0.045823 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 47 nu = 0.411509 obj = -20.606013, rho = -0.000558 nSV = 43, nBSV = 39 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 77 nu = 0.329319 obj = -24.171408, rho = 0.003577 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 53 nu = 0.273400 obj = -28.619623, rho = -0.034791 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 86 nu = 0.223511 obj = -33.838687, rho = -0.032460 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *...* optimization finished, #iter = 311 nu = 0.181963 obj = -40.010106, rho = -0.035949 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.154084 obj = -46.941800, rho = 0.003779 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 54 nu = 0.125157 obj = -54.707588, rho = -0.060855 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*....* optimization finished, #iter = 536 nu = 0.103256 obj = -62.154525, rho = -0.200926 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -0.914475, rho = 0.897920 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -1.299329, rho = 0.853164 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.835710, rho = 0.788783 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.571655, rho = 0.696175 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.556588, rho = 0.562963 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 51.1% (511/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.820897, rho = 0.371344 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 69% (69/100) (classification) Accuracy = 68.7% (687/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.324064, rho = 0.095711 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 93% (93/100) (classification) Accuracy = 94.7% (947/1000) (classification) * optimization finished, #iter = 47 nu = 0.863041 obj = -7.951746, rho = -0.002747 nSV = 89, nBSV = 85 Total nSV = 89 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 47 nu = 0.771097 obj = -9.837501, rho = -0.099677 nSV = 79, nBSV = 75 Total nSV = 79 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 45 nu = 0.660000 obj = -12.063405, rho = -0.084626 nSV = 68, nBSV = 65 Total nSV = 68 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 45 nu = 0.560220 obj = -14.767290, rho = -0.129963 nSV = 58, nBSV = 54 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 49 nu = 0.484644 obj = -18.002823, rho = -0.155684 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 67 nu = 0.408342 obj = -21.903568, rho = -0.122616 nSV = 44, nBSV = 36 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 55 nu = 0.345962 obj = -26.646215, rho = -0.134095 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 52 nu = 0.300407 obj = -31.816088, rho = -0.123065 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 60 nu = 0.247914 obj = -37.683968, rho = -0.190852 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 46 nu = 0.202876 obj = -44.853180, rho = -0.139188 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 73 nu = 0.164731 obj = -53.878374, rho = -0.122779 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 52 nu = 0.137933 obj = -65.592604, rho = -0.063313 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 89 nu = 0.118988 obj = -79.243198, rho = -0.082191 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -0.805347, rho = -0.960626 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 52.1% (521/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -1.149211, rho = -0.943362 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 52.1% (521/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -1.633961, rho = -0.918529 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 52.1% (521/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -2.310806, rho = -0.882808 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 52.1% (521/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -3.242114, rho = -0.831426 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 52.1% (521/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -4.494229, rho = -0.757514 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 52.1% (521/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -6.114232, rho = -0.651197 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 65% (65/100) (classification) Accuracy = 59.9% (599/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -8.069804, rho = -0.498264 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 87% (87/100) (classification) Accuracy = 88.2% (882/1000) (classification) * optimization finished, #iter = 43 nu = 0.796147 obj = -10.201728, rho = -0.332247 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 96% (96/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 43 nu = 0.682443 obj = -12.489768, rho = -0.278796 nSV = 71, nBSV = 66 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 38 nu = 0.596310 obj = -15.242607, rho = -0.215335 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 46 nu = 0.512358 obj = -18.186652, rho = -0.220223 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 34 nu = 0.421767 obj = -21.709480, rho = -0.172049 nSV = 44, nBSV = 41 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 44 nu = 0.343558 obj = -25.850525, rho = -0.153849 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.287138 obj = -30.915260, rho = -0.087704 nSV = 35, nBSV = 26 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 60 nu = 0.241555 obj = -36.775815, rho = -0.138756 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 74 nu = 0.196199 obj = -43.843543, rho = -0.108490 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 80 nu = 0.160861 obj = -52.822905, rho = -0.102722 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.131658 obj = -64.573799, rho = -0.074098 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 57 nu = 0.111832 obj = -80.235280, rho = -0.047284 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -0.896648, rho = -0.927295 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.275054, rho = -0.895417 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.803627, rho = -0.849562 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -2.531371, rho = -0.783603 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -3.510779, rho = -0.688724 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -4.780116, rho = -0.552245 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 65% (65/100) (classification) Accuracy = 59.7% (597/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -6.317364, rho = -0.355927 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 88% (88/100) (classification) Accuracy = 89.3% (893/1000) (classification) * optimization finished, #iter = 45 nu = 0.878542 obj = -7.998198, rho = -0.158104 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 96% (96/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 46 nu = 0.772082 obj = -9.891395, rho = -0.128227 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 59 nu = 0.664410 obj = -12.078138, rho = -0.115328 nSV = 68, nBSV = 64 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 51 nu = 0.564507 obj = -14.680281, rho = -0.127487 nSV = 62, nBSV = 55 Total nSV = 62 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 35 nu = 0.483355 obj = -17.860317, rho = -0.172075 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 42 nu = 0.411231 obj = -21.566656, rho = -0.162515 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 45 nu = 0.347502 obj = -25.757724, rho = -0.168565 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 49 nu = 0.294192 obj = -30.709038, rho = -0.182600 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *..* optimization finished, #iter = 203 nu = 0.242669 obj = -35.865966, rho = -0.256099 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 140 nu = 0.193274 obj = -42.076684, rho = -0.293389 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*..* optimization finished, #iter = 301 nu = 0.158335 obj = -49.361864, rho = -0.340231 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 191 nu = 0.137072 obj = -56.919854, rho = -0.543319 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 90 nu = 0.106267 obj = -64.735439, rho = -0.566108 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -0.949359, rho = 0.829402 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 52.3% (523/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.346281, rho = 0.754603 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 52.3% (523/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.896572, rho = 0.647009 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 52.3% (523/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -2.645387, rho = 0.492240 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 52.3% (523/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -3.634065, rho = 0.269613 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 60% (60/100) (classification) Accuracy = 63.6% (636/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.873200, rho = -0.050625 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 89% (89/100) (classification) Accuracy = 87.7% (877/1000) (classification) * optimization finished, #iter = 48 nu = 0.944059 obj = -6.305993, rho = -0.313432 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 92% (92/100) (classification) Accuracy = 94.8% (948/1000) (classification) * optimization finished, #iter = 46 nu = 0.859548 obj = -7.990781, rho = -0.282032 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 93% (93/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 39 nu = 0.758496 obj = -10.018708, rho = -0.295140 nSV = 76, nBSV = 74 Total nSV = 76 Accuracy = 94% (94/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 43 nu = 0.661637 obj = -12.490976, rho = -0.271507 nSV = 68, nBSV = 63 Total nSV = 68 Accuracy = 96% (96/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 67 nu = 0.578340 obj = -15.481097, rho = -0.189277 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 95% (95/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 41 nu = 0.493747 obj = -19.109099, rho = -0.151008 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 96% (96/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 40 nu = 0.422624 obj = -23.657775, rho = -0.090154 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 96% (96/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 57 nu = 0.359326 obj = -29.371910, rho = -0.124767 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 96% (96/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 38 nu = 0.318100 obj = -36.667547, rho = -0.230642 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 66 nu = 0.272693 obj = -45.643610, rho = -0.250121 nSV = 32, nBSV = 23 Total nSV = 32 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.235976 obj = -57.065536, rho = -0.253510 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 71 nu = 0.199549 obj = -72.088680, rho = -0.321355 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 73 nu = 0.175904 obj = -91.869643, rho = -0.326091 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 79 nu = 0.166413 obj = -115.462617, rho = -0.255817 nSV = 18, nBSV = 13 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -0.860600, rho = -0.930932 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -1.225694, rho = -0.900649 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -1.737782, rho = -0.857088 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -2.447328, rho = -0.794428 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -3.411967, rho = -0.704296 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -4.683667, rho = -0.574644 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 59% (59/100) (classification) Accuracy = 54.2% (542/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -6.273162, rho = -0.388147 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 87% (87/100) (classification) Accuracy = 85.1% (851/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -8.097530, rho = -0.207875 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 97% (97/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 51 nu = 0.761774 obj = -10.194760, rho = -0.174971 nSV = 79, nBSV = 73 Total nSV = 79 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 40 nu = 0.680446 obj = -12.748393, rho = -0.178658 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 49 nu = 0.596521 obj = -15.654748, rho = -0.119816 nSV = 63, nBSV = 57 Total nSV = 63 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 61 nu = 0.509095 obj = -19.164420, rho = -0.057748 nSV = 54, nBSV = 47 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 40 nu = 0.437310 obj = -23.374042, rho = -0.120788 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 38 nu = 0.369660 obj = -28.427603, rho = -0.141480 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 23 nu = 0.314002 obj = -34.578163, rho = -0.191632 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 42 nu = 0.262286 obj = -41.907197, rho = -0.240274 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 54 nu = 0.218412 obj = -50.940312, rho = -0.217121 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 63 nu = 0.186577 obj = -62.262259, rho = -0.257087 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.161359 obj = -76.150088, rho = -0.387688 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 148 nu = 0.132413 obj = -92.684866, rho = -0.254767 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 49 nu = 0.900000 obj = -0.875894, rho = 0.909209 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -1.244727, rho = 0.869380 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -1.759020, rho = 0.812110 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -2.465172, rho = 0.729730 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -3.411346, rho = 0.611230 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -4.628380, rho = 0.440774 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 64% (64/100) (classification) Accuracy = 57.5% (575/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -6.081083, rho = 0.195582 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 92% (92/100) (classification) Accuracy = 87% (870/1000) (classification) * optimization finished, #iter = 43 nu = 0.846860 obj = -7.624123, rho = 0.041665 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 97% (97/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 39 nu = 0.740000 obj = -9.366307, rho = 0.016312 nSV = 75, nBSV = 73 Total nSV = 75 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 44 nu = 0.650515 obj = -11.300797, rho = -0.116276 nSV = 67, nBSV = 64 Total nSV = 67 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.545478 obj = -13.342923, rho = -0.101375 nSV = 58, nBSV = 51 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.453322 obj = -15.657727, rho = -0.088198 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 60 nu = 0.365380 obj = -18.164433, rho = -0.103056 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 47 nu = 0.292172 obj = -21.292029, rho = -0.058215 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 56 nu = 0.247552 obj = -24.784090, rho = 0.020423 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *..* optimization finished, #iter = 281 nu = 0.194212 obj = -28.682426, rho = 0.062817 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 92 nu = 0.152423 obj = -33.779828, rho = 0.049482 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 46 nu = 0.126302 obj = -40.177732, rho = 0.122463 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 188 nu = 0.106623 obj = -47.136122, rho = 0.251665 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.084365 obj = -55.465267, rho = 0.298955 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -0.915591, rho = 0.871263 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.301636, rho = 0.814819 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.840485, rho = 0.733626 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.581535, rho = 0.616834 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -3.577033, rho = 0.449437 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -4.863201, rho = 0.208043 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 73% (73/100) (classification) Accuracy = 72.5% (725/1000) (classification) * optimization finished, #iter = 49 nu = 0.938184 obj = -6.411645, rho = -0.131864 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 92% (92/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -8.234403, rho = -0.098480 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 40 nu = 0.775251 obj = -10.443681, rho = -0.091090 nSV = 78, nBSV = 76 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 41 nu = 0.689027 obj = -13.130675, rho = -0.103524 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 37 nu = 0.616493 obj = -16.302851, rho = -0.090450 nSV = 63, nBSV = 60 Total nSV = 63 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 35 nu = 0.533355 obj = -20.002230, rho = -0.042416 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 38 nu = 0.451152 obj = -24.425295, rho = -0.079453 nSV = 47, nBSV = 43 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 71 nu = 0.394879 obj = -29.402000, rho = 0.072953 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 49 nu = 0.322877 obj = -35.276991, rho = 0.038922 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 49 nu = 0.276091 obj = -42.465514, rho = 0.093292 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 59 nu = 0.228489 obj = -50.819375, rho = 0.026601 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.192649 obj = -59.884330, rho = -0.026246 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 153 nu = 0.163915 obj = -69.713464, rho = -0.064018 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.131933 obj = -78.893869, rho = -0.029682 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -0.840946, rho = -0.941709 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -1.197642, rho = -0.916151 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -1.697882, rho = -0.879387 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -2.390869, rho = -0.826505 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -3.332690, rho = -0.750436 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -4.573636, rho = -0.641014 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 58% (58/100) (classification) Accuracy = 53.6% (536/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -6.123174, rho = -0.483617 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 84% (84/100) (classification) Accuracy = 76% (760/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -7.895557, rho = -0.322679 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 94% (94/100) (classification) Accuracy = 93.6% (936/1000) (classification) * optimization finished, #iter = 41 nu = 0.760000 obj = -9.831280, rho = -0.296212 nSV = 77, nBSV = 75 Total nSV = 77 Accuracy = 94% (94/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 65 nu = 0.655673 obj = -12.119500, rho = -0.258736 nSV = 69, nBSV = 62 Total nSV = 69 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 68 nu = 0.574618 obj = -14.807245, rho = -0.229853 nSV = 61, nBSV = 54 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 60 nu = 0.487983 obj = -17.956655, rho = -0.241363 nSV = 52, nBSV = 45 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.406179 obj = -21.779356, rho = -0.224586 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 48 nu = 0.342598 obj = -26.357674, rho = -0.166096 nSV = 38, nBSV = 30 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 59 nu = 0.286992 obj = -32.183476, rho = -0.176444 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 31 nu = 0.246742 obj = -39.552798, rho = -0.265785 nSV = 27, nBSV = 22 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 83 nu = 0.207855 obj = -47.994187, rho = -0.311044 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 58 nu = 0.172360 obj = -59.113370, rho = -0.317900 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 60 nu = 0.152786 obj = -72.475437, rho = -0.259581 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 63 nu = 0.131931 obj = -87.025314, rho = -0.178687 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -0.918259, rho = 0.914504 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -1.307158, rho = 0.877018 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -1.851911, rho = 0.823097 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -2.605176, rho = 0.745060 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -3.625949, rho = 0.633963 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -4.964415, rho = 0.473475 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 56% (56/100) (classification) Accuracy = 52.1% (521/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -6.621029, rho = 0.242268 nSV = 95, nBSV = 92 Total nSV = 95 Accuracy = 88% (88/100) (classification) Accuracy = 81.4% (814/1000) (classification) * optimization finished, #iter = 49 nu = 0.916465 obj = -8.460471, rho = -0.011802 nSV = 93, nBSV = 90 Total nSV = 93 Accuracy = 96% (96/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 46 nu = 0.818429 obj = -10.514015, rho = -0.024854 nSV = 84, nBSV = 80 Total nSV = 84 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 41 nu = 0.693833 obj = -12.969758, rho = -0.034537 nSV = 71, nBSV = 68 Total nSV = 71 Accuracy = 96% (96/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 50 nu = 0.611093 obj = -15.845214, rho = -0.106260 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 50 nu = 0.516116 obj = -19.279225, rho = -0.071713 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 36 nu = 0.447000 obj = -23.288570, rho = -0.074671 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 68 nu = 0.366725 obj = -28.003542, rho = -0.123954 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.304769 obj = -33.949723, rho = -0.147140 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 56 nu = 0.251985 obj = -41.755572, rho = -0.138091 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.219713 obj = -51.281981, rho = -0.063478 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 56 nu = 0.182189 obj = -63.546438, rho = -0.008800 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 232 nu = 0.157552 obj = -79.140832, rho = 0.010629 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *..* optimization finished, #iter = 210 nu = 0.135865 obj = -99.843961, rho = 0.146179 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -0.914766, rho = 0.883713 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 47.4% (474/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -1.299930, rho = 0.832727 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 47.4% (474/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -1.836955, rho = 0.759386 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 47.4% (474/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.574231, rho = 0.653889 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 47.4% (474/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.561918, rho = 0.502137 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 47.7% (477/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -4.831926, rho = 0.283849 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 69% (69/100) (classification) Accuracy = 64.6% (646/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -6.346884, rho = -0.030147 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 92% (92/100) (classification) Accuracy = 91.6% (916/1000) (classification) * optimization finished, #iter = 44 nu = 0.879348 obj = -8.026588, rho = -0.054598 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 94% (94/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 41 nu = 0.780000 obj = -9.961961, rho = -0.110887 nSV = 80, nBSV = 76 Total nSV = 80 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 60 nu = 0.673478 obj = -12.158664, rho = -0.080574 nSV = 70, nBSV = 63 Total nSV = 70 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 42 nu = 0.576640 obj = -14.839844, rho = -0.174555 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 53 nu = 0.489250 obj = -17.819116, rho = -0.219998 nSV = 53, nBSV = 46 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 29 nu = 0.407430 obj = -21.488353, rho = -0.186776 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 69 nu = 0.346912 obj = -25.585339, rho = -0.129084 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 99.4% (994/1000) (classification) * optimization finished, #iter = 43 nu = 0.283584 obj = -30.403486, rho = -0.184114 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 99.3% (993/1000) (classification) * optimization finished, #iter = 73 nu = 0.234014 obj = -36.215832, rho = -0.182130 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 63 nu = 0.198100 obj = -43.205420, rho = -0.236221 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) *..*..* optimization finished, #iter = 369 nu = 0.164028 obj = -51.111744, rho = -0.162264 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 171 nu = 0.133770 obj = -60.264039, rho = -0.130936 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 66 nu = 0.115312 obj = -70.706093, rho = -0.179430 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.951066, rho = -0.902872 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.349813, rho = -0.860286 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.903881, rho = -0.799029 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.660511, rho = -0.710913 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.665357, rho = -0.584163 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 54% (54/100) (classification) Accuracy = 51.9% (519/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.937948, rho = -0.401839 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 82% (82/100) (classification) Accuracy = 82% (820/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -6.410895, rho = -0.328978 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 94% (94/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 51 nu = 0.889046 obj = -8.040853, rho = -0.226471 nSV = 92, nBSV = 87 Total nSV = 92 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 44 nu = 0.779487 obj = -9.833391, rho = -0.143079 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 49 nu = 0.658806 obj = -12.019210, rho = -0.129485 nSV = 68, nBSV = 63 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 50 nu = 0.562634 obj = -14.703284, rho = -0.081208 nSV = 59, nBSV = 52 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 43 nu = 0.482917 obj = -17.914509, rho = -0.054162 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 43 nu = 0.410642 obj = -21.608749, rho = -0.007846 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 41 nu = 0.341957 obj = -25.949601, rho = 0.085529 nSV = 36, nBSV = 32 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 51 nu = 0.288304 obj = -31.218326, rho = 0.098867 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 66 nu = 0.235439 obj = -37.696576, rho = 0.112434 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 34 nu = 0.197718 obj = -46.104653, rho = 0.077779 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.169531 obj = -55.769281, rho = 0.120305 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 37 nu = 0.143469 obj = -67.958141, rho = 0.128020 nSV = 17, nBSV = 12 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 75 nu = 0.125222 obj = -80.605994, rho = 0.126015 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -0.877898, rho = -0.931438 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -1.248872, rho = -0.901376 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -1.767599, rho = -0.858780 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -2.482924, rho = -0.796861 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -3.448077, rho = -0.707795 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -4.704380, rho = -0.579678 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 64% (64/100) (classification) Accuracy = 56.2% (562/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -6.238339, rho = -0.395388 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 91% (91/100) (classification) Accuracy = 87.6% (876/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -7.934503, rho = -0.262652 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 38 nu = 0.752681 obj = -9.910863, rho = -0.280509 nSV = 76, nBSV = 74 Total nSV = 76 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 45 nu = 0.666759 obj = -12.252785, rho = -0.222912 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 37 nu = 0.576161 obj = -15.020258, rho = -0.199820 nSV = 59, nBSV = 56 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 36 nu = 0.498061 obj = -18.208451, rho = -0.210506 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 83 nu = 0.423905 obj = -21.799435, rho = -0.181367 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 78 nu = 0.345630 obj = -26.080504, rho = -0.162675 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 61 nu = 0.296022 obj = -30.932827, rho = -0.117684 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) *..* optimization finished, #iter = 207 nu = 0.240197 obj = -36.238505, rho = -0.027425 nSV = 30, nBSV = 20 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 195 nu = 0.197440 obj = -42.941246, rho = -0.037739 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 211 nu = 0.159381 obj = -51.068615, rho = -0.010428 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.131167 obj = -61.451770, rho = -0.033652 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 92 nu = 0.112052 obj = -73.816056, rho = -0.062410 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 51 nu = 0.960000 obj = -0.938070, rho = 0.918483 nSV = 98, nBSV = 95 Total nSV = 98 Accuracy = 52% (52/100) (classification) Accuracy = 52.1% (521/1000) (classification) * optimization finished, #iter = 51 nu = 0.960000 obj = -1.335536, rho = 0.882743 nSV = 98, nBSV = 95 Total nSV = 98 Accuracy = 52% (52/100) (classification) Accuracy = 52.1% (521/1000) (classification) * optimization finished, #iter = 51 nu = 0.960000 obj = -1.892483, rho = 0.831331 nSV = 98, nBSV = 95 Total nSV = 98 Accuracy = 52% (52/100) (classification) Accuracy = 52.1% (521/1000) (classification) * optimization finished, #iter = 51 nu = 0.960000 obj = -2.663027, rho = 0.757378 nSV = 98, nBSV = 95 Total nSV = 98 Accuracy = 52% (52/100) (classification) Accuracy = 52.1% (521/1000) (classification) * optimization finished, #iter = 51 nu = 0.960000 obj = -3.708106, rho = 0.651001 nSV = 98, nBSV = 95 Total nSV = 98 Accuracy = 52% (52/100) (classification) Accuracy = 52.1% (521/1000) (classification) * optimization finished, #iter = 51 nu = 0.960000 obj = -5.080407, rho = 0.498249 nSV = 98, nBSV = 95 Total nSV = 98 Accuracy = 55% (55/100) (classification) Accuracy = 56.7% (567/1000) (classification) * optimization finished, #iter = 51 nu = 0.960000 obj = -6.783345, rho = 0.278256 nSV = 98, nBSV = 95 Total nSV = 98 Accuracy = 89% (89/100) (classification) Accuracy = 89.4% (894/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -8.682060, rho = 0.025503 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 46 nu = 0.826213 obj = -10.799054, rho = 0.030921 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 48 nu = 0.732144 obj = -13.269476, rho = 0.014552 nSV = 75, nBSV = 69 Total nSV = 75 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 33 nu = 0.631558 obj = -16.123982, rho = -0.060277 nSV = 64, nBSV = 62 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 70 nu = 0.528430 obj = -19.429115, rho = -0.098625 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 34 nu = 0.445872 obj = -23.486525, rho = -0.028115 nSV = 47, nBSV = 43 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.376069 obj = -28.112589, rho = -0.048949 nSV = 43, nBSV = 34 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 55 nu = 0.307253 obj = -33.781675, rho = -0.054949 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 78 nu = 0.258145 obj = -40.832015, rho = -0.123533 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.217753 obj = -49.393624, rho = -0.173246 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 72 nu = 0.177327 obj = -60.189680, rho = -0.194811 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 86 nu = 0.150211 obj = -74.171325, rho = -0.269960 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) ...*..* optimization finished, #iter = 510 nu = 0.131049 obj = -91.372344, rho = -0.164145 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -0.933787, rho = 0.891676 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -1.326674, rho = 0.844181 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.874146, rho = 0.775863 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -2.625085, rho = 0.677590 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -3.629619, rho = 0.533954 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -4.918004, rho = 0.329616 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 67% (67/100) (classification) Accuracy = 67.3% (673/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -6.447311, rho = 0.035686 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 97% (97/100) (classification) Accuracy = 94.5% (945/1000) (classification) * optimization finished, #iter = 50 nu = 0.886555 obj = -8.122358, rho = -0.025461 nSV = 92, nBSV = 88 Total nSV = 92 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 47 nu = 0.788062 obj = -10.000263, rho = -0.043025 nSV = 81, nBSV = 77 Total nSV = 81 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 69 nu = 0.686559 obj = -12.140998, rho = -0.047869 nSV = 71, nBSV = 64 Total nSV = 71 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 37 nu = 0.585246 obj = -14.597960, rho = -0.013012 nSV = 60, nBSV = 57 Total nSV = 60 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 39 nu = 0.498499 obj = -17.125921, rho = -0.063513 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 52 nu = 0.403126 obj = -19.862868, rho = -0.002381 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.327871 obj = -22.891748, rho = -0.050580 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 35 nu = 0.258738 obj = -26.555811, rho = -0.054253 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 51 nu = 0.208276 obj = -30.879113, rho = -0.074107 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.170291 obj = -36.019981, rho = -0.025855 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 73 nu = 0.139226 obj = -41.755194, rho = -0.024326 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 68 nu = 0.112979 obj = -47.795120, rho = 0.024115 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 73 nu = 0.088819 obj = -54.653947, rho = -0.053449 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.966943, rho = -0.008619 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.370050, rho = -0.012399 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.927609, rho = -0.017835 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.683508, rho = -0.025654 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.675398, rho = -0.036902 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -4.904720, rho = -0.053082 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 49 nu = 0.961889 obj = -6.288450, rho = -0.082323 nSV = 98, nBSV = 96 Total nSV = 98 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 45 nu = 0.878733 obj = -7.831081, rho = -0.032553 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 51 nu = 0.767530 obj = -9.511000, rho = 0.003210 nSV = 79, nBSV = 75 Total nSV = 79 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 45 nu = 0.654329 obj = -11.385506, rho = 0.015902 nSV = 68, nBSV = 63 Total nSV = 68 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 47 nu = 0.544675 obj = -13.519801, rho = 0.016503 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 38 nu = 0.457973 obj = -16.057087, rho = -0.008126 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 33 nu = 0.371537 obj = -18.969836, rho = 0.084099 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 53 nu = 0.308109 obj = -22.246808, rho = 0.023364 nSV = 33, nBSV = 29 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 69 nu = 0.249980 obj = -26.099430, rho = -0.032275 nSV = 30, nBSV = 21 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 49 nu = 0.199987 obj = -30.746979, rho = -0.062620 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 46 nu = 0.172175 obj = -36.214923, rho = -0.010115 nSV = 19, nBSV = 14 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.140229 obj = -41.275692, rho = -0.136752 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 49 nu = 0.110860 obj = -47.157111, rho = -0.150334 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 48 nu = 0.090975 obj = -53.583632, rho = -0.074178 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -0.901800, rho = -0.937185 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 47.5% (475/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.285716, rho = -0.909644 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 47.5% (475/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.825688, rho = -0.870027 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 47.5% (475/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -2.577018, rho = -0.813041 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 47.5% (475/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -3.605228, rho = -0.731068 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 47.5% (475/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -4.975543, rho = -0.613155 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -6.721731, rho = -0.443543 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 79% (79/100) (classification) Accuracy = 78.9% (789/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -8.768097, rho = -0.212176 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 47 nu = 0.842730 obj = -11.060801, rho = -0.181950 nSV = 86, nBSV = 82 Total nSV = 86 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 42 nu = 0.738926 obj = -13.747483, rho = -0.114206 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.640409 obj = -16.929454, rho = -0.106444 nSV = 66, nBSV = 61 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 39 nu = 0.546696 obj = -20.771959, rho = -0.064577 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 199 nu = 0.475105 obj = -25.233594, rho = -0.118125 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 54 nu = 0.400508 obj = -30.479307, rho = -0.131774 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 46 nu = 0.338337 obj = -36.501076, rho = -0.144059 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 41 nu = 0.286127 obj = -43.226129, rho = -0.087523 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 94 nu = 0.236558 obj = -50.668188, rho = -0.156102 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 76 nu = 0.199156 obj = -58.739045, rho = 0.056725 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *..* optimization finished, #iter = 225 nu = 0.163065 obj = -65.738133, rho = 0.014469 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 188 nu = 0.127858 obj = -71.848488, rho = -0.080366 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -0.875792, rho = 0.899771 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -1.244516, rho = 0.855826 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -1.758584, rho = 0.792613 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -2.464269, rho = 0.701684 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -3.409479, rho = 0.570887 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -4.624515, rho = 0.382743 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 69% (69/100) (classification) Accuracy = 63.2% (632/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -6.073086, rho = 0.112106 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 93% (93/100) (classification) Accuracy = 91.7% (917/1000) (classification) * optimization finished, #iter = 46 nu = 0.840000 obj = -7.616718, rho = 0.001438 nSV = 86, nBSV = 82 Total nSV = 86 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 42 nu = 0.731627 obj = -9.430036, rho = -0.007619 nSV = 74, nBSV = 71 Total nSV = 74 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 33 nu = 0.640000 obj = -11.617880, rho = 0.038925 nSV = 65, nBSV = 63 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 42 nu = 0.553139 obj = -14.009787, rho = 0.060076 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 49 nu = 0.465301 obj = -16.773205, rho = -0.065596 nSV = 49, nBSV = 42 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 46 nu = 0.380130 obj = -20.119925, rho = -0.086903 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 40 nu = 0.321777 obj = -24.092493, rho = -0.094378 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 56 nu = 0.264357 obj = -29.051707, rho = -0.139860 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 73 nu = 0.217636 obj = -35.493069, rho = -0.187456 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 61 nu = 0.190199 obj = -43.575144, rho = -0.345312 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 70 nu = 0.159440 obj = -53.202547, rho = -0.431416 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.134383 obj = -65.293553, rho = -0.538879 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 26 nu = 0.116158 obj = -80.702748, rho = -0.580626 nSV = 13, nBSV = 8 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.970884, rho = -0.024016 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.378205, rho = -0.034545 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.944483, rho = -0.049691 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.718424, rho = -0.071479 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.747644, rho = -0.102818 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -5.054208, rho = -0.147899 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -6.590033, rho = -0.140144 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 98% (98/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 50 nu = 0.914286 obj = -8.287152, rho = -0.164141 nSV = 93, nBSV = 90 Total nSV = 93 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 50 nu = 0.805901 obj = -10.219495, rho = -0.117392 nSV = 83, nBSV = 78 Total nSV = 83 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 58 nu = 0.709133 obj = -12.381627, rho = -0.100737 nSV = 73, nBSV = 65 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 38 nu = 0.593122 obj = -14.830167, rho = -0.160146 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 53 nu = 0.495362 obj = -17.539335, rho = -0.199313 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 35 nu = 0.413520 obj = -20.681432, rho = -0.246421 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 43 nu = 0.336787 obj = -24.229956, rho = -0.206328 nSV = 35, nBSV = 30 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 61 nu = 0.270543 obj = -28.333803, rho = -0.257324 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 80 nu = 0.219708 obj = -33.218632, rho = -0.208728 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.182039 obj = -39.022138, rho = -0.156428 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 59 nu = 0.150582 obj = -45.493555, rho = -0.171619 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 230 nu = 0.120733 obj = -52.495583, rho = -0.240979 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 54 nu = 0.096742 obj = -60.894824, rho = -0.255474 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -0.932849, rho = 0.863759 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.324732, rho = 0.804024 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.870130, rho = 0.718099 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -2.616775, rho = 0.594499 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -3.612404, rho = 0.416708 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 54% (54/100) (classification) Accuracy = 52.1% (521/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -4.882385, rho = 0.160963 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 74% (74/100) (classification) Accuracy = 76.9% (769/1000) (classification) * optimization finished, #iter = 52 nu = 0.931390 obj = -6.385705, rho = -0.077333 nSV = 96, nBSV = 92 Total nSV = 96 Accuracy = 93% (93/100) (classification) Accuracy = 95.1% (951/1000) (classification) * optimization finished, #iter = 50 nu = 0.873658 obj = -8.136796, rho = -0.288791 nSV = 90, nBSV = 85 Total nSV = 90 Accuracy = 95% (95/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 42 nu = 0.770013 obj = -10.219258, rho = -0.213418 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 94% (94/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 45 nu = 0.679113 obj = -12.736019, rho = -0.219488 nSV = 70, nBSV = 64 Total nSV = 70 Accuracy = 95% (95/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 41 nu = 0.585456 obj = -15.814077, rho = -0.296053 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 36 nu = 0.503547 obj = -19.709043, rho = -0.284942 nSV = 53, nBSV = 50 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 37 nu = 0.441886 obj = -24.390275, rho = -0.286226 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 71 nu = 0.385692 obj = -29.908410, rho = -0.224235 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 170 nu = 0.322669 obj = -36.485792, rho = -0.187975 nSV = 37, nBSV = 28 Total nSV = 37 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 70 nu = 0.270110 obj = -45.041325, rho = -0.175213 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 96% (96/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 62 nu = 0.229000 obj = -56.327368, rho = -0.198556 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 93 nu = 0.198893 obj = -71.059783, rho = -0.219223 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 68 nu = 0.176309 obj = -89.949694, rho = -0.396951 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 140 nu = 0.154207 obj = -113.550764, rho = -0.394295 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -0.933760, rho = -0.915199 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.326618, rho = -0.878019 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.874030, rho = -0.824536 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.624846, rho = -0.747604 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.629105, rho = -0.636941 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 56% (56/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.916942, rho = -0.477757 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 81% (81/100) (classification) Accuracy = 77.8% (778/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.453185, rho = -0.299709 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 95% (95/100) (classification) Accuracy = 94.4% (944/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -8.295980, rho = -0.267341 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 97% (97/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 54 nu = 0.789452 obj = -10.384992, rho = -0.286867 nSV = 81, nBSV = 77 Total nSV = 81 Accuracy = 96% (96/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 53 nu = 0.700000 obj = -12.929081, rho = -0.210845 nSV = 71, nBSV = 68 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 37 nu = 0.617723 obj = -15.702394, rho = -0.187013 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 62 nu = 0.510926 obj = -18.960004, rho = -0.195268 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 35 nu = 0.434914 obj = -23.020250, rho = -0.251328 nSV = 45, nBSV = 41 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 60 nu = 0.366395 obj = -27.691345, rho = -0.302844 nSV = 39, nBSV = 35 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 56 nu = 0.304154 obj = -33.405270, rho = -0.258403 nSV = 33, nBSV = 29 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 41 nu = 0.262386 obj = -39.669546, rho = -0.208721 nSV = 28, nBSV = 24 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 47 nu = 0.215051 obj = -46.762438, rho = -0.230380 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 92 nu = 0.182553 obj = -54.135498, rho = -0.212138 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *...* optimization finished, #iter = 300 nu = 0.149875 obj = -60.595168, rho = -0.188704 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*..* optimization finished, #iter = 362 nu = 0.114727 obj = -67.305472, rho = -0.142853 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -0.934443, rho = 0.864157 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -1.328031, rho = 0.804597 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.876954, rho = 0.718923 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -2.630895, rho = 0.595684 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -3.641621, rho = 0.418412 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 52.1% (521/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -4.942839, rho = 0.163415 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 77% (77/100) (classification) Accuracy = 78.5% (785/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -6.498697, rho = -0.203386 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 94% (94/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 47 nu = 0.877426 obj = -8.300302, rho = -0.180770 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 46 nu = 0.799723 obj = -10.423883, rho = -0.148009 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 43 nu = 0.693870 obj = -12.964569, rho = -0.135039 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 40 nu = 0.612150 obj = -15.976595, rho = -0.066111 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 66 nu = 0.523338 obj = -19.467631, rho = -0.049664 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 45 nu = 0.440681 obj = -23.570260, rho = -0.010886 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 63 nu = 0.367527 obj = -28.564212, rho = 0.066207 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 97% (97/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 70 nu = 0.305213 obj = -35.178895, rho = 0.051945 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 97% (97/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 65 nu = 0.260824 obj = -43.717551, rho = 0.044833 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.227900 obj = -54.431573, rho = 0.108275 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 67 nu = 0.195003 obj = -67.762635, rho = 0.172771 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 48 nu = 0.173982 obj = -83.518385, rho = 0.224585 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.150126 obj = -101.119553, rho = 0.159259 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -0.857408, rho = 0.902451 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -1.219090, rho = 0.859681 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -1.724120, rho = 0.798128 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 56% (56/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -2.419056, rho = 0.709620 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 49 nu = 0.880000 obj = -3.353474, rho = 0.582406 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 56% (56/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -4.562638, rho = 0.399223 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 65% (65/100) (classification) Accuracy = 57.1% (571/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -6.022736, rho = 0.135813 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 89% (89/100) (classification) Accuracy = 84.4% (844/1000) (classification) * optimization finished, #iter = 41 nu = 0.820000 obj = -7.667287, rho = -0.002930 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 95% (95/100) (classification) Accuracy = 93.9% (939/1000) (classification) * optimization finished, #iter = 43 nu = 0.734741 obj = -9.568455, rho = -0.070758 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 97% (97/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 39 nu = 0.642330 obj = -11.784094, rho = -0.026456 nSV = 66, nBSV = 63 Total nSV = 66 Accuracy = 97% (97/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 34 nu = 0.557275 obj = -14.431664, rho = -0.001124 nSV = 57, nBSV = 54 Total nSV = 57 Accuracy = 97% (97/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 67 nu = 0.472393 obj = -17.406938, rho = 0.043984 nSV = 51, nBSV = 42 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 44 nu = 0.399524 obj = -21.023230, rho = 0.139149 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 54 nu = 0.331730 obj = -25.376787, rho = 0.178021 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 46 nu = 0.274904 obj = -30.771670, rho = 0.192217 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 52 nu = 0.236134 obj = -37.448062, rho = 0.164415 nSV = 25, nBSV = 21 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 88 nu = 0.198669 obj = -45.574053, rho = 0.104732 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 89 nu = 0.166150 obj = -55.691534, rho = 0.135431 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 83 nu = 0.140587 obj = -68.259105, rho = 0.207209 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.117945 obj = -84.809347, rho = 0.202768 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -0.896093, rho = 0.912426 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.273906, rho = 0.874029 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.801251, rho = 0.818796 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -2.526455, rho = 0.739348 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -3.500606, rho = 0.625065 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -4.759067, rho = 0.460674 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 69% (69/100) (classification) Accuracy = 60% (600/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -6.273811, rho = 0.224207 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 93% (93/100) (classification) Accuracy = 87.1% (871/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -7.947152, rho = 0.095145 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 97% (97/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 43 nu = 0.768728 obj = -9.852154, rho = 0.103308 nSV = 78, nBSV = 74 Total nSV = 78 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 43 nu = 0.669561 obj = -12.046049, rho = 0.158620 nSV = 70, nBSV = 65 Total nSV = 70 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 40 nu = 0.583788 obj = -14.418582, rho = 0.172755 nSV = 60, nBSV = 57 Total nSV = 60 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 43 nu = 0.483147 obj = -16.911448, rho = 0.136361 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 42 nu = 0.391716 obj = -19.805395, rho = 0.160664 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.323129 obj = -23.143421, rho = 0.156475 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.262778 obj = -27.020745, rho = 0.223822 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 78 nu = 0.212946 obj = -31.552165, rho = 0.218185 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 69 nu = 0.169456 obj = -37.013024, rho = 0.257740 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 43 nu = 0.143573 obj = -43.408103, rho = 0.186884 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 95 nu = 0.118098 obj = -49.944602, rho = 0.203953 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 66 nu = 0.092699 obj = -57.367708, rho = 0.324611 nSV = 12, nBSV = 7 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.962923, rho = -0.014448 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 93.2% (932/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.361732, rho = -0.020783 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 93.2% (932/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.910398, rho = -0.029896 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 93.2% (932/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.647896, rho = -0.043004 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 93.2% (932/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.601712, rho = -0.061858 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 93.2% (932/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -4.752253, rho = -0.088980 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 93.2% (932/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.055693, rho = -0.191290 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 45 nu = 0.852677 obj = -7.504003, rho = -0.213931 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 40 nu = 0.733925 obj = -9.165938, rho = -0.173874 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 51 nu = 0.621646 obj = -11.057212, rho = -0.170015 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 58 nu = 0.519684 obj = -13.387026, rho = -0.146668 nSV = 55, nBSV = 48 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 42 nu = 0.440919 obj = -16.206417, rho = -0.082621 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 28 nu = 0.369513 obj = -19.575737, rho = -0.089248 nSV = 39, nBSV = 35 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 63 nu = 0.320088 obj = -23.361508, rho = -0.113804 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 82 nu = 0.262168 obj = -27.554765, rho = -0.074512 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.211087 obj = -32.718618, rho = -0.081965 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 28 nu = 0.181151 obj = -39.042437, rho = 0.034974 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 99 nu = 0.151086 obj = -45.418211, rho = 0.188303 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 288 nu = 0.121372 obj = -52.386724, rho = 0.253401 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 152 nu = 0.103352 obj = -58.660673, rho = 0.385280 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -0.908545, rho = -0.912683 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.287059, rho = -0.874399 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.810322, rho = -0.819329 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -2.519125, rho = -0.740114 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -3.447896, rho = -0.626166 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 60% (60/100) (classification) Accuracy = 58.1% (581/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.595998, rho = -0.462259 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 86% (86/100) (classification) Accuracy = 82.3% (823/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -5.897687, rho = -0.407274 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 94% (94/100) (classification) Accuracy = 90.6% (906/1000) (classification) * optimization finished, #iter = 43 nu = 0.812564 obj = -7.390911, rho = -0.356811 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 98% (98/100) (classification) Accuracy = 93.7% (937/1000) (classification) * optimization finished, #iter = 40 nu = 0.726715 obj = -9.095201, rho = -0.267315 nSV = 74, nBSV = 71 Total nSV = 74 Accuracy = 99% (99/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 48 nu = 0.611347 obj = -11.069540, rho = -0.222199 nSV = 63, nBSV = 60 Total nSV = 63 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 59 nu = 0.522993 obj = -13.441806, rho = -0.142314 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 35 nu = 0.440275 obj = -16.286300, rho = -0.117536 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 31 nu = 0.380000 obj = -19.501753, rho = -0.043805 nSV = 39, nBSV = 36 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 74 nu = 0.314077 obj = -23.179118, rho = -0.035453 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 28 nu = 0.262930 obj = -27.633438, rho = -0.011667 nSV = 28, nBSV = 25 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 86 nu = 0.217172 obj = -32.078446, rho = -0.091221 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.176567 obj = -37.069990, rho = -0.082157 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 67 nu = 0.142275 obj = -43.008578, rho = -0.161052 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 93 nu = 0.118447 obj = -48.688805, rho = -0.332273 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 98 nu = 0.092452 obj = -54.154790, rho = -0.413008 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -0.917163, rho = -0.932129 nSV = 96, nBSV = 92 Total nSV = 96 Accuracy = 53% (53/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -1.304892, rho = -0.902533 nSV = 96, nBSV = 92 Total nSV = 96 Accuracy = 53% (53/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -1.847221, rho = -0.859799 nSV = 96, nBSV = 92 Total nSV = 96 Accuracy = 53% (53/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -2.595472, rho = -0.798328 nSV = 96, nBSV = 92 Total nSV = 96 Accuracy = 53% (53/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -3.605870, rho = -0.709904 nSV = 96, nBSV = 92 Total nSV = 96 Accuracy = 53% (53/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -4.922868, rho = -0.582712 nSV = 96, nBSV = 92 Total nSV = 96 Accuracy = 59% (59/100) (classification) Accuracy = 59.9% (599/1000) (classification) * optimization finished, #iter = 51 nu = 0.940000 obj = -6.535057, rho = -0.399365 nSV = 96, nBSV = 92 Total nSV = 96 Accuracy = 90% (90/100) (classification) Accuracy = 88.5% (885/1000) (classification) * optimization finished, #iter = 49 nu = 0.897755 obj = -8.305944, rho = -0.217554 nSV = 92, nBSV = 88 Total nSV = 92 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 44 nu = 0.800807 obj = -10.317954, rho = -0.223710 nSV = 82, nBSV = 79 Total nSV = 82 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 46 nu = 0.713597 obj = -12.540919, rho = -0.070936 nSV = 73, nBSV = 70 Total nSV = 73 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 54 nu = 0.593931 obj = -15.019901, rho = -0.067385 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 43 nu = 0.503169 obj = -17.949377, rho = -0.109712 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 52 nu = 0.412370 obj = -21.279282, rho = -0.097970 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 74 nu = 0.344341 obj = -25.273457, rho = -0.034085 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 50 nu = 0.286915 obj = -29.728009, rho = -0.038958 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 67 nu = 0.233160 obj = -34.514403, rho = -0.085910 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 80 nu = 0.194565 obj = -40.070945, rho = -0.070590 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.153952 obj = -45.664370, rho = -0.044432 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 161 nu = 0.124847 obj = -52.386479, rho = -0.049322 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.098768 obj = -58.875790, rho = -0.065917 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -0.916013, rho = -0.930708 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.302511, rho = -0.900327 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.842294, rho = -0.856626 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -2.585279, rho = -0.793764 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -3.584779, rho = -0.703339 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.879227, rho = -0.573268 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 76% (76/100) (classification) Accuracy = 67.8% (678/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.444758, rho = -0.386168 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 92% (92/100) (classification) Accuracy = 92.3% (923/1000) (classification) * optimization finished, #iter = 50 nu = 0.876495 obj = -8.192791, rho = -0.299442 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 94% (94/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 54 nu = 0.780045 obj = -10.268104, rho = -0.254169 nSV = 81, nBSV = 75 Total nSV = 81 Accuracy = 95% (95/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.685486 obj = -12.729497, rho = -0.197843 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 95% (95/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 51 nu = 0.583553 obj = -15.774183, rho = -0.177452 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 95% (95/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 52 nu = 0.492099 obj = -19.777968, rho = -0.206990 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 95% (95/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 32 nu = 0.431220 obj = -25.050416, rho = -0.259667 nSV = 45, nBSV = 41 Total nSV = 45 Accuracy = 95% (95/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 64 nu = 0.378899 obj = -31.624273, rho = -0.238770 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 96% (96/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 70 nu = 0.332704 obj = -40.005627, rho = -0.352295 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 96% (96/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 53 nu = 0.288192 obj = -50.851390, rho = -0.438664 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 96% (96/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 80 nu = 0.248181 obj = -65.662460, rho = -0.472452 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 96% (96/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 63 nu = 0.225842 obj = -85.733022, rho = -0.611656 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 52 nu = 0.205527 obj = -111.640259, rho = -0.615633 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 96% (96/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 74 nu = 0.194102 obj = -143.085771, rho = -0.782161 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 96% (96/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -0.917082, rho = -0.933571 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.304723, rho = -0.904445 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.846871, rho = -0.862549 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.594749, rho = -0.802284 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.604373, rho = -0.715596 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -4.919771, rho = -0.590899 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 69% (69/100) (classification) Accuracy = 62.4% (624/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -6.528648, rho = -0.411528 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 92% (92/100) (classification) Accuracy = 89.4% (894/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -8.385058, rho = -0.366007 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 96% (96/100) (classification) Accuracy = 94% (940/1000) (classification) * optimization finished, #iter = 44 nu = 0.797107 obj = -10.576342, rho = -0.313271 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 96% (96/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 44 nu = 0.703303 obj = -13.167452, rho = -0.246173 nSV = 72, nBSV = 67 Total nSV = 72 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 47 nu = 0.615774 obj = -16.271080, rho = -0.265614 nSV = 65, nBSV = 59 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 61 nu = 0.520000 obj = -20.016952, rho = -0.249492 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 44 nu = 0.450577 obj = -24.441272, rho = -0.189908 nSV = 49, nBSV = 42 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 61 nu = 0.381457 obj = -29.795245, rho = -0.125372 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 46 nu = 0.330037 obj = -36.473020, rho = -0.078700 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 297 nu = 0.277336 obj = -43.861989, rho = -0.131005 nSV = 33, nBSV = 23 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 61 nu = 0.236569 obj = -53.291567, rho = -0.219059 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 99 nu = 0.199122 obj = -63.612009, rho = -0.228592 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.167496 obj = -75.845511, rho = -0.215949 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *...* optimization finished, #iter = 340 nu = 0.141405 obj = -87.703506, rho = -0.104464 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -0.900925, rho = -0.929925 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 49.4% (494/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.283906, rho = -0.899201 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 49.4% (494/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.821942, rho = -0.855006 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 49.4% (494/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -2.569266, rho = -0.791433 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 49.4% (494/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -3.589189, rho = -0.699987 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 49.4% (494/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -4.942356, rho = -0.568447 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 55% (55/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -6.653062, rho = -0.379233 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 74% (74/100) (classification) Accuracy = 77.8% (778/1000) (classification) * optimization finished, #iter = 49 nu = 0.908691 obj = -8.629083, rho = -0.135285 nSV = 93, nBSV = 89 Total nSV = 93 Accuracy = 93% (93/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 50 nu = 0.816480 obj = -10.840879, rho = -0.009679 nSV = 84, nBSV = 80 Total nSV = 84 Accuracy = 96% (96/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 40 nu = 0.719608 obj = -13.516727, rho = 0.010328 nSV = 74, nBSV = 70 Total nSV = 74 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 39 nu = 0.632893 obj = -16.736779, rho = 0.034244 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.540169 obj = -20.529378, rho = 0.044215 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.466104 obj = -25.109355, rho = 0.009552 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 91 nu = 0.389669 obj = -30.624867, rho = 0.016914 nSV = 43, nBSV = 34 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 62 nu = 0.331006 obj = -37.733469, rho = -0.030307 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 80 nu = 0.282189 obj = -46.345256, rho = -0.146099 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 57 nu = 0.248689 obj = -57.044253, rho = -0.154901 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 81 nu = 0.207913 obj = -69.175347, rho = -0.246311 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.170895 obj = -85.219043, rho = -0.243228 nSV = 23, nBSV = 12 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.148162 obj = -106.909318, rho = -0.366621 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -0.896907, rho = 0.884466 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.7% (487/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -1.275592, rho = 0.833810 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.7% (487/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -1.804739, rho = 0.760944 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.7% (487/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -2.533672, rho = 0.656130 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.7% (487/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -3.515540, rho = 0.505360 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.7% (487/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -4.789966, rho = 0.288485 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 66% (66/100) (classification) Accuracy = 65.2% (652/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -6.337746, rho = -0.023479 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 91% (91/100) (classification) Accuracy = 91% (910/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -8.100716, rho = -0.148212 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 93% (93/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 45 nu = 0.761038 obj = -10.260317, rho = -0.143672 nSV = 78, nBSV = 74 Total nSV = 78 Accuracy = 95% (95/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 42 nu = 0.667631 obj = -12.903098, rho = -0.121507 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 96% (96/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 45 nu = 0.586330 obj = -16.174885, rho = -0.177389 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 95% (95/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 38 nu = 0.522758 obj = -20.132866, rho = -0.311701 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 95% (95/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 69 nu = 0.448608 obj = -24.907583, rho = -0.322967 nSV = 50, nBSV = 43 Total nSV = 50 Accuracy = 95% (95/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 88 nu = 0.382552 obj = -30.801320, rho = -0.325631 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 95% (95/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 67 nu = 0.338894 obj = -38.038951, rho = -0.245443 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 93 nu = 0.283770 obj = -46.826842, rho = -0.187888 nSV = 34, nBSV = 25 Total nSV = 34 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.238933 obj = -58.307127, rho = -0.136125 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 96% (96/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 59 nu = 0.209975 obj = -73.180791, rho = -0.050077 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 172 nu = 0.182073 obj = -90.570192, rho = 0.034651 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 144 nu = 0.155045 obj = -113.008425, rho = 0.132618 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -0.879691, rho = -0.946312 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -1.252582, rho = -0.922772 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -1.775274, rho = -0.888912 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -2.498804, rho = -0.840205 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -3.480935, rho = -0.770143 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -4.772369, rho = -0.669363 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 58% (58/100) (classification) Accuracy = 55.2% (552/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -6.379016, rho = -0.524395 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 84% (84/100) (classification) Accuracy = 82.3% (823/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -8.181972, rho = -0.345640 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 96% (96/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 42 nu = 0.785149 obj = -10.226473, rho = -0.320900 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 47 nu = 0.692764 obj = -12.512937, rho = -0.230373 nSV = 72, nBSV = 67 Total nSV = 72 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 40 nu = 0.594346 obj = -15.126510, rho = -0.226700 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 74 nu = 0.500631 obj = -18.097761, rho = -0.188934 nSV = 54, nBSV = 47 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 58 nu = 0.415727 obj = -21.679375, rho = -0.157521 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 43 nu = 0.343891 obj = -25.872551, rho = -0.145609 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 57 nu = 0.289847 obj = -30.935715, rho = -0.065210 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 87 nu = 0.239186 obj = -36.707831, rho = -0.071825 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 93 nu = 0.196936 obj = -43.814581, rho = -0.069898 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 51 nu = 0.164131 obj = -52.202488, rho = -0.044460 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 55 nu = 0.140173 obj = -61.382152, rho = -0.024605 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.119652 obj = -69.803243, rho = 0.216506 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 43 nu = 0.820000 obj = -0.804157, rho = 0.922501 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 43 nu = 0.820000 obj = -1.146747, rho = 0.888521 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -1.628863, rho = 0.839643 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -2.300259, rho = 0.769335 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 41 nu = 0.820000 obj = -3.220291, rho = 0.668200 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 41 nu = 0.820000 obj = -4.449074, rho = 0.522722 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 52.4% (524/1000) (classification) * optimization finished, #iter = 41 nu = 0.820000 obj = -6.020801, rho = 0.313460 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 75% (75/100) (classification) Accuracy = 71.1% (711/1000) (classification) * optimization finished, #iter = 41 nu = 0.820000 obj = -7.876481, rho = 0.012446 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 96% (96/100) (classification) Accuracy = 94.8% (948/1000) (classification) * optimization finished, #iter = 38 nu = 0.760000 obj = -9.900444, rho = -0.131560 nSV = 76, nBSV = 76 Total nSV = 76 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 47 nu = 0.666248 obj = -12.211896, rho = -0.092222 nSV = 70, nBSV = 64 Total nSV = 70 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 46 nu = 0.582897 obj = -14.842898, rho = -0.024381 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 35 nu = 0.485845 obj = -17.950007, rho = -0.002549 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 52 nu = 0.407661 obj = -21.610141, rho = 0.052850 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 34 nu = 0.351131 obj = -26.027185, rho = 0.101595 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 30 nu = 0.289668 obj = -31.041908, rho = 0.138399 nSV = 32, nBSV = 28 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 72 nu = 0.241574 obj = -36.814176, rho = 0.143091 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 123 nu = 0.199198 obj = -43.560968, rho = 0.254657 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 161 nu = 0.169556 obj = -51.068315, rho = 0.217697 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 163 nu = 0.135806 obj = -59.087658, rho = 0.201584 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 140 nu = 0.107822 obj = -68.415165, rho = 0.214947 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -0.934026, rho = -0.923939 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 46.7% (467/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.327168, rho = -0.890589 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 46.7% (467/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.875168, rho = -0.842618 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 46.7% (467/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.627201, rho = -0.773614 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 46.7% (467/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.633977, rho = -0.674356 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 46.8% (468/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.927022, rho = -0.531577 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 75% (75/100) (classification) Accuracy = 71% (710/1000) (classification) * optimization finished, #iter = 48 nu = 0.954863 obj = -6.466291, rho = -0.365797 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 90% (90/100) (classification) Accuracy = 92.4% (924/1000) (classification) * optimization finished, #iter = 48 nu = 0.888564 obj = -8.236570, rho = -0.267504 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 95% (95/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 41 nu = 0.780000 obj = -10.319669, rho = -0.271372 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 97% (97/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 59 nu = 0.680709 obj = -12.794317, rho = -0.317286 nSV = 72, nBSV = 64 Total nSV = 72 Accuracy = 96% (96/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 40 nu = 0.594697 obj = -15.876881, rho = -0.240734 nSV = 61, nBSV = 58 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 39 nu = 0.507943 obj = -19.607328, rho = -0.238801 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 33 nu = 0.442014 obj = -24.020599, rho = -0.156051 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 68 nu = 0.373953 obj = -29.339686, rho = -0.223282 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 66 nu = 0.329770 obj = -35.591253, rho = -0.115627 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 174 nu = 0.270669 obj = -42.712713, rho = -0.063930 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 75 nu = 0.230801 obj = -51.357170, rho = -0.143484 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.190842 obj = -61.587714, rho = -0.132941 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 142 nu = 0.159518 obj = -73.384971, rho = -0.077607 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 228 nu = 0.135098 obj = -86.338951, rho = 0.006736 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.966373, rho = -0.025604 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 96% (96/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.368870, rho = -0.036830 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 96% (96/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.925168, rho = -0.052979 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 96% (96/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.678457, rho = -0.076207 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 96% (96/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.664948, rho = -0.109620 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 96% (96/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -4.883097, rho = -0.157683 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 96% (96/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -6.247869, rho = -0.218521 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 97% (97/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 47 nu = 0.865181 obj = -7.799067, rho = -0.169316 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 42 nu = 0.760754 obj = -9.590468, rho = -0.187918 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 49 nu = 0.645673 obj = -11.660763, rho = -0.188225 nSV = 68, nBSV = 63 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 52 nu = 0.554118 obj = -14.061810, rho = -0.209130 nSV = 58, nBSV = 54 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.477366 obj = -16.776890, rho = -0.123650 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 51 nu = 0.387391 obj = -19.755735, rho = -0.153072 nSV = 43, nBSV = 34 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 51 nu = 0.322365 obj = -23.299989, rho = -0.178555 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 53 nu = 0.256866 obj = -27.573565, rho = -0.164638 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 59 nu = 0.218270 obj = -32.529545, rho = -0.075146 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 43 nu = 0.178036 obj = -38.243884, rho = -0.037295 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.145821 obj = -44.312498, rho = -0.020535 nSV = 21, nBSV = 10 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 154 nu = 0.118011 obj = -51.530563, rho = -0.112056 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.099607 obj = -59.148499, rho = -0.103989 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -0.859355, rho = 0.923543 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -1.223119, rho = 0.890021 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 48 nu = 0.880000 obj = -1.732455, rho = 0.841800 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 48 nu = 0.880000 obj = -2.436305, rho = 0.772438 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 48 nu = 0.880000 obj = -3.389159, rho = 0.672663 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 48 nu = 0.880000 obj = -4.636475, rho = 0.529142 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 57% (57/100) (classification) Accuracy = 54.6% (546/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -6.175514, rho = 0.322694 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 84% (84/100) (classification) Accuracy = 80.3% (803/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -7.869017, rho = 0.070085 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 98% (98/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 47 nu = 0.766293 obj = -9.630358, rho = 0.019926 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 37 nu = 0.661325 obj = -11.650577, rho = 0.008567 nSV = 68, nBSV = 65 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 59 nu = 0.568523 obj = -13.864661, rho = -0.025415 nSV = 60, nBSV = 53 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 52 nu = 0.467497 obj = -16.232882, rho = -0.017903 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 39 nu = 0.387662 obj = -18.822412, rho = 0.051741 nSV = 40, nBSV = 36 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.310711 obj = -21.604205, rho = 0.085733 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 66 nu = 0.247094 obj = -24.791994, rho = 0.045898 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.195244 obj = -28.525515, rho = 0.048829 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.154268 obj = -33.195515, rho = 0.061258 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 45 nu = 0.127071 obj = -39.205441, rho = 0.175321 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 44 nu = 0.102760 obj = -45.980959, rho = 0.097471 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 62 nu = 0.086716 obj = -53.399392, rho = -0.236431 nSV = 11, nBSV = 5 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.953923, rho = -0.899566 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 52.7% (527/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.355723, rho = -0.855530 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 52.7% (527/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.916110, rho = -0.792188 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 52.7% (527/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.685814, rho = -0.701072 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 52.7% (527/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.717713, rho = -0.570008 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 53.1% (531/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -5.046280, rho = -0.381478 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 79% (79/100) (classification) Accuracy = 81.9% (819/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -6.635050, rho = -0.110286 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -8.397254, rho = -0.153135 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 47 nu = 0.820949 obj = -10.329157, rho = -0.079597 nSV = 84, nBSV = 80 Total nSV = 84 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 44 nu = 0.701342 obj = -12.531860, rho = -0.036891 nSV = 72, nBSV = 70 Total nSV = 72 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 48 nu = 0.606274 obj = -15.051174, rho = -0.054341 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 53 nu = 0.504450 obj = -17.825717, rho = -0.017123 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 78 nu = 0.411430 obj = -21.043488, rho = -0.035197 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.343910 obj = -24.797903, rho = -0.064417 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.275815 obj = -29.207123, rho = -0.075964 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.229469 obj = -34.411097, rho = -0.110501 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.184210 obj = -40.474598, rho = -0.099754 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 53 nu = 0.162783 obj = -47.029423, rho = -0.053892 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 258 nu = 0.135877 obj = -50.510748, rho = 0.063419 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 166 nu = 0.099893 obj = -52.766494, rho = 0.085535 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -0.857339, rho = -0.945065 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -1.218948, rho = -0.920979 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -1.723823, rho = -0.886333 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -2.418445, rho = -0.836495 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -3.352205, rho = -0.764807 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -4.560011, rho = -0.661686 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 68% (68/100) (classification) Accuracy = 61.5% (615/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -6.017301, rho = -0.513353 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 94% (94/100) (classification) Accuracy = 86% (860/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -7.579984, rho = -0.365770 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 96% (96/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 49 nu = 0.730969 obj = -9.339838, rho = -0.353793 nSV = 76, nBSV = 71 Total nSV = 76 Accuracy = 96% (96/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 56 nu = 0.632764 obj = -11.407387, rho = -0.325093 nSV = 67, nBSV = 60 Total nSV = 67 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 37 nu = 0.540000 obj = -13.804417, rho = -0.317946 nSV = 55, nBSV = 53 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 61 nu = 0.458665 obj = -16.475418, rho = -0.250157 nSV = 50, nBSV = 43 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 48 nu = 0.384262 obj = -19.541375, rho = -0.199618 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.307590 obj = -23.277945, rho = -0.212627 nSV = 36, nBSV = 27 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 56 nu = 0.255141 obj = -28.025688, rho = -0.325185 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 72 nu = 0.216001 obj = -33.661358, rho = -0.285021 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) *..* optimization finished, #iter = 238 nu = 0.180959 obj = -40.212100, rho = -0.249838 nSV = 25, nBSV = 15 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 98 nu = 0.153244 obj = -47.754541, rho = -0.251968 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 114 nu = 0.129384 obj = -56.143665, rho = -0.367055 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 171 nu = 0.102377 obj = -65.272968, rho = -0.363424 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -0.933994, rho = 0.892156 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 46.3% (463/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -1.327102, rho = 0.844872 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 46.3% (463/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -1.875033, rho = 0.776855 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 46.3% (463/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -2.626920, rho = 0.679018 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 46.3% (463/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -3.633396, rho = 0.538283 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 46.4% (464/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -4.925820, rho = 0.335843 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 69% (69/100) (classification) Accuracy = 65.2% (652/1000) (classification) * optimization finished, #iter = 48 nu = 0.958489 obj = -6.463522, rho = 0.059153 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 98% (98/100) (classification) Accuracy = 93.5% (935/1000) (classification) * optimization finished, #iter = 48 nu = 0.893276 obj = -8.180179, rho = 0.031034 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 40 nu = 0.790942 obj = -10.172169, rho = 0.022244 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 40 nu = 0.685743 obj = -12.405295, rho = 0.040299 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 74 nu = 0.588690 obj = -14.980620, rho = -0.002275 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 70 nu = 0.501882 obj = -17.870303, rho = -0.071112 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 42 nu = 0.413432 obj = -21.190904, rho = -0.109150 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 56 nu = 0.339908 obj = -25.032982, rho = -0.123490 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 78 nu = 0.279342 obj = -29.670643, rho = -0.140730 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 66 nu = 0.228456 obj = -35.257693, rho = -0.162533 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.188946 obj = -42.080599, rho = -0.157687 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 81 nu = 0.154368 obj = -50.559747, rho = -0.104425 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 64 nu = 0.128248 obj = -61.232742, rho = -0.068260 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.108652 obj = -74.217058, rho = -0.021974 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.950972, rho = -0.893603 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.349619, rho = -0.846954 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.903479, rho = -0.779850 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.659679, rho = -0.683326 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.663637, rho = -0.544480 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 67% (67/100) (classification) Accuracy = 64.7% (647/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.934389, rho = -0.344757 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 93% (93/100) (classification) Accuracy = 91.4% (914/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.460839, rho = -0.353902 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 91% (91/100) (classification) Accuracy = 94.2% (942/1000) (classification) * optimization finished, #iter = 43 nu = 0.856386 obj = -8.342432, rho = -0.315436 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 94% (94/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 42 nu = 0.785829 obj = -10.649273, rho = -0.351537 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 96% (96/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 39 nu = 0.703752 obj = -13.346929, rho = -0.240387 nSV = 75, nBSV = 70 Total nSV = 75 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 33 nu = 0.620000 obj = -16.420050, rho = -0.258012 nSV = 63, nBSV = 61 Total nSV = 63 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 60 nu = 0.523604 obj = -20.153748, rho = -0.274582 nSV = 58, nBSV = 50 Total nSV = 58 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 46 nu = 0.451693 obj = -24.869510, rho = -0.285282 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 55 nu = 0.385516 obj = -30.746246, rho = -0.346103 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 96% (96/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 32 nu = 0.324404 obj = -38.264361, rho = -0.337811 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 40 nu = 0.289737 obj = -47.857084, rho = -0.482471 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 97% (97/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 99 nu = 0.257735 obj = -58.962282, rho = -0.447444 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 97% (97/100) (classification) Accuracy = 99% (990/1000) (classification) .*.* optimization finished, #iter = 226 nu = 0.212880 obj = -71.700647, rho = -0.335868 nSV = 27, nBSV = 17 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) *....* optimization finished, #iter = 416 nu = 0.178219 obj = -88.861340, rho = -0.268177 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.154858 obj = -110.951751, rho = -0.143659 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -0.839090, rho = -0.943432 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -1.193801, rho = -0.918630 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -1.689937, rho = -0.882482 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -2.374429, rho = -0.830956 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -3.298673, rho = -0.756838 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -4.503250, rho = -0.650224 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 62% (62/100) (classification) Accuracy = 54% (540/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -5.977537, rho = -0.496865 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 90% (90/100) (classification) Accuracy = 82.6% (826/1000) (classification) * optimization finished, #iter = 45 nu = 0.833507 obj = -7.580600, rho = -0.327480 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 99% (99/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 61 nu = 0.735166 obj = -9.348153, rho = -0.292550 nSV = 75, nBSV = 70 Total nSV = 75 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 33 nu = 0.645784 obj = -11.390556, rho = -0.181913 nSV = 66, nBSV = 64 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 38 nu = 0.552959 obj = -13.523875, rho = -0.215738 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 42 nu = 0.463967 obj = -15.847574, rho = -0.186911 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 50 nu = 0.375080 obj = -18.303048, rho = -0.197159 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 57 nu = 0.293267 obj = -21.326933, rho = -0.230397 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.244123 obj = -25.094432, rho = -0.273097 nSV = 26, nBSV = 22 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 54 nu = 0.196722 obj = -29.106897, rho = -0.235844 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 64 nu = 0.162460 obj = -33.859287, rho = -0.269242 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.130641 obj = -39.182302, rho = -0.300273 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 79 nu = 0.105253 obj = -44.710417, rho = -0.268798 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 99 nu = 0.081374 obj = -51.564374, rho = -0.320826 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -0.936355, rho = 0.883639 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 51.9% (519/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.331988, rho = 0.832620 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 51.9% (519/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.885141, rho = 0.759232 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 51.9% (519/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.647836, rho = 0.653667 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 51.9% (519/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.676674, rho = 0.501818 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 51.9% (519/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -5.015368, rho = 0.283390 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 65% (65/100) (classification) Accuracy = 68.3% (683/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -6.648771, rho = -0.030808 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 92% (92/100) (classification) Accuracy = 95.1% (951/1000) (classification) * optimization finished, #iter = 51 nu = 0.900199 obj = -8.489637, rho = -0.189496 nSV = 93, nBSV = 89 Total nSV = 93 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.819267 obj = -10.614089, rho = -0.144839 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 52 nu = 0.715797 obj = -13.089613, rho = -0.105134 nSV = 74, nBSV = 68 Total nSV = 74 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 37 nu = 0.620000 obj = -16.035686, rho = -0.092950 nSV = 64, nBSV = 61 Total nSV = 64 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 60 nu = 0.527523 obj = -19.324560, rho = -0.120383 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 51 nu = 0.442994 obj = -23.198432, rho = -0.112595 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 45 nu = 0.366312 obj = -27.897440, rho = -0.039091 nSV = 41, nBSV = 33 Total nSV = 41 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 80 nu = 0.311669 obj = -33.683636, rho = -0.089169 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 39 nu = 0.257114 obj = -40.593560, rho = -0.041661 nSV = 32, nBSV = 23 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 52 nu = 0.219457 obj = -48.561087, rho = -0.227096 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 78 nu = 0.180632 obj = -57.674671, rho = -0.153864 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 230 nu = 0.147332 obj = -69.592472, rho = -0.081517 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*....* optimization finished, #iter = 583 nu = 0.122802 obj = -84.465075, rho = -0.095242 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.963190, rho = -0.036489 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 96% (96/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.362285, rho = -0.052488 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 96% (96/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.911542, rho = -0.075502 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 96% (96/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.650264, rho = -0.108605 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 96% (96/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.606613, rho = -0.156223 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 96% (96/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -4.762394, rho = -0.224720 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 96% (96/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 49 nu = 0.919334 obj = -6.108245, rho = -0.229848 nSV = 93, nBSV = 90 Total nSV = 93 Accuracy = 97% (97/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 45 nu = 0.826496 obj = -7.755169, rho = -0.235187 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 40 nu = 0.741920 obj = -9.745015, rho = -0.151559 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 50 nu = 0.655065 obj = -11.984527, rho = -0.034870 nSV = 68, nBSV = 63 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 34 nu = 0.566398 obj = -14.685166, rho = -0.017757 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 75 nu = 0.478694 obj = -17.783765, rho = -0.024292 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 39 nu = 0.403758 obj = -21.524166, rho = -0.051773 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 37 nu = 0.344329 obj = -26.004823, rho = -0.077528 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.284031 obj = -31.353111, rho = -0.077607 nSV = 32, nBSV = 23 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 65 nu = 0.233538 obj = -38.299242, rho = -0.093490 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 36 nu = 0.205539 obj = -47.089790, rho = -0.033389 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 76 nu = 0.180219 obj = -56.565588, rho = -0.234079 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 57 nu = 0.149120 obj = -67.092357, rho = -0.156791 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 70 nu = 0.121458 obj = -79.378847, rho = -0.196856 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -0.950207, rho = 0.835522 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.348036, rho = 0.763406 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.900203, rho = 0.659672 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -2.652902, rho = 0.510455 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -3.649614, rho = 0.295814 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 61% (61/100) (classification) Accuracy = 57.4% (574/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.905373, rho = -0.012936 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 91% (91/100) (classification) Accuracy = 88.8% (888/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -6.372455, rho = -0.243707 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 95% (95/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 47 nu = 0.873598 obj = -8.066275, rho = -0.173665 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 97% (97/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 42 nu = 0.780403 obj = -9.982717, rho = -0.138371 nSV = 81, nBSV = 77 Total nSV = 81 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 59 nu = 0.679929 obj = -12.114512, rho = -0.144906 nSV = 72, nBSV = 66 Total nSV = 72 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 42 nu = 0.570777 obj = -14.616461, rho = -0.123092 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 62 nu = 0.483364 obj = -17.504472, rho = -0.121130 nSV = 53, nBSV = 46 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 33 nu = 0.399228 obj = -21.006527, rho = -0.174869 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 30 nu = 0.341282 obj = -25.201207, rho = -0.113177 nSV = 36, nBSV = 32 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 62 nu = 0.284395 obj = -29.704460, rho = -0.147475 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 50 nu = 0.232825 obj = -35.028195, rho = -0.231749 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 59 nu = 0.191855 obj = -41.056767, rho = -0.265165 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 56 nu = 0.152961 obj = -48.257613, rho = -0.236578 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 69 nu = 0.127858 obj = -57.227599, rho = -0.173293 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.111268 obj = -65.836854, rho = 0.021615 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.948602, rho = 0.863150 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.344713, rho = 0.803149 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.893329, rho = 0.716839 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.638677, rho = 0.592687 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.620180, rho = 0.414101 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 53% (53/100) (classification) Accuracy = 52.5% (525/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.844470, rho = 0.157214 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 82% (82/100) (classification) Accuracy = 78.2% (782/1000) (classification) * optimization finished, #iter = 50 nu = 0.969135 obj = -6.219183, rho = -0.133375 nSV = 98, nBSV = 95 Total nSV = 98 Accuracy = 98% (98/100) (classification) Accuracy = 94.8% (948/1000) (classification) * optimization finished, #iter = 45 nu = 0.872950 obj = -7.698472, rho = -0.153285 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 41 nu = 0.760000 obj = -9.334354, rho = -0.163203 nSV = 77, nBSV = 75 Total nSV = 77 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 42 nu = 0.646576 obj = -11.148546, rho = -0.150947 nSV = 66, nBSV = 63 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 41 nu = 0.536794 obj = -13.198673, rho = -0.158807 nSV = 56, nBSV = 50 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 39 nu = 0.449367 obj = -15.583188, rho = -0.113957 nSV = 47, nBSV = 44 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 59 nu = 0.362752 obj = -18.150036, rho = -0.160242 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 40 nu = 0.296285 obj = -21.246321, rho = -0.157153 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 50 nu = 0.241568 obj = -24.649158, rho = -0.091732 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 29 nu = 0.191536 obj = -28.763812, rho = -0.093781 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 74 nu = 0.160148 obj = -33.146258, rho = -0.026145 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.125141 obj = -38.445746, rho = 0.065084 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 160 nu = 0.100047 obj = -45.081340, rho = 0.084125 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 40 nu = 0.081896 obj = -53.658409, rho = 0.037285 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -0.933416, rho = -0.919464 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.325905, rho = -0.884153 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.872556, rho = -0.833360 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.621795, rho = -0.760296 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.622792, rho = -0.655198 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 55% (55/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.903878, rho = -0.504019 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 79% (79/100) (classification) Accuracy = 77.1% (771/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.428114, rho = -0.343575 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 95% (95/100) (classification) Accuracy = 94.1% (941/1000) (classification) * optimization finished, #iter = 47 nu = 0.871678 obj = -8.206716, rho = -0.287339 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 44 nu = 0.783687 obj = -10.299897, rho = -0.234069 nSV = 80, nBSV = 76 Total nSV = 80 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 42 nu = 0.690359 obj = -12.775825, rho = -0.222661 nSV = 71, nBSV = 66 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 33 nu = 0.600000 obj = -15.712512, rho = -0.283713 nSV = 61, nBSV = 59 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 49 nu = 0.506516 obj = -19.254204, rho = -0.232907 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 36 nu = 0.438236 obj = -23.582651, rho = -0.187650 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 46 nu = 0.369921 obj = -28.650292, rho = -0.140080 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 63 nu = 0.308040 obj = -34.934889, rho = -0.146730 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 52 nu = 0.271855 obj = -42.112109, rho = -0.262606 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 91 nu = 0.221059 obj = -50.725383, rho = -0.250814 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 69 nu = 0.190625 obj = -61.292545, rho = -0.256430 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 38 nu = 0.160344 obj = -72.879452, rho = -0.362156 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) *...* optimization finished, #iter = 362 nu = 0.135046 obj = -85.940587, rho = -0.307689 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -0.879160, rho = -0.929424 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -1.251483, rho = -0.898479 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -1.773000, rho = -0.853740 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -2.494098, rho = -0.789613 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -3.471198, rho = -0.697368 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -4.752223, rho = -0.565356 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 56% (56/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -6.337332, rho = -0.374786 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 89% (89/100) (classification) Accuracy = 85% (850/1000) (classification) * optimization finished, #iter = 45 nu = 0.885030 obj = -8.089060, rho = -0.161708 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 66 nu = 0.785840 obj = -9.939555, rho = -0.076700 nSV = 81, nBSV = 76 Total nSV = 81 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.671478 obj = -12.107769, rho = -0.098095 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 35 nu = 0.570356 obj = -14.717795, rho = -0.083935 nSV = 59, nBSV = 56 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 32 nu = 0.485025 obj = -17.790616, rho = -0.150408 nSV = 51, nBSV = 48 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 76 nu = 0.420824 obj = -21.143125, rho = -0.134631 nSV = 46, nBSV = 38 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 42 nu = 0.340624 obj = -24.783085, rho = -0.165176 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 89 nu = 0.285061 obj = -29.021364, rho = -0.110244 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 81 nu = 0.231949 obj = -33.806363, rho = -0.134442 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 38 nu = 0.185549 obj = -38.651686, rho = -0.165367 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 54 nu = 0.145182 obj = -44.741576, rho = -0.200106 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 52 nu = 0.116238 obj = -52.639460, rho = -0.167600 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 54 nu = 0.099377 obj = -61.622251, rho = -0.437853 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -0.916153, rho = -0.927934 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.302801, rho = -0.896336 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.842895, rho = -0.850884 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -2.586522, rho = -0.785505 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -3.587350, rho = -0.691459 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.884547, rho = -0.556180 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 64% (64/100) (classification) Accuracy = 62.6% (626/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.455765, rho = -0.361587 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 96% (96/100) (classification) Accuracy = 92.2% (922/1000) (classification) * optimization finished, #iter = 50 nu = 0.892306 obj = -8.169684, rho = -0.236060 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 52 nu = 0.808504 obj = -10.039244, rho = -0.157020 nSV = 83, nBSV = 77 Total nSV = 83 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 38 nu = 0.680000 obj = -12.177163, rho = -0.144945 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 46 nu = 0.575113 obj = -14.706841, rho = -0.092863 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 47 nu = 0.482562 obj = -17.763515, rho = -0.103419 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 54 nu = 0.409690 obj = -21.415156, rho = -0.163221 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 56 nu = 0.334045 obj = -25.909044, rho = -0.200015 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.280817 obj = -31.688365, rho = -0.230595 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 46 nu = 0.241055 obj = -38.862018, rho = -0.269659 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 40 nu = 0.202130 obj = -47.681551, rho = -0.292634 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 36 nu = 0.178865 obj = -58.182201, rho = -0.160780 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.150889 obj = -69.706446, rho = -0.125424 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 83 nu = 0.129960 obj = -83.529029, rho = -0.022732 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.950697, rho = 0.880825 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.349050, rho = 0.828573 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.902301, rho = 0.753411 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.657242, rho = 0.645294 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -3.658595, rho = 0.489774 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.923955, rho = 0.266065 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 78% (78/100) (classification) Accuracy = 77% (770/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -6.381943, rho = -0.037346 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -7.995870, rho = -0.070000 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 39 nu = 0.780000 obj = -9.843577, rho = -0.006159 nSV = 78, nBSV = 78 Total nSV = 78 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 47 nu = 0.672687 obj = -11.864772, rho = -0.053180 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 39 nu = 0.559245 obj = -14.262876, rho = 0.002428 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 51 nu = 0.480591 obj = -17.145826, rho = -0.003602 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 78 nu = 0.404982 obj = -20.211835, rho = -0.058094 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 61 nu = 0.325774 obj = -23.717352, rho = -0.029735 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.264120 obj = -27.925357, rho = 0.014406 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 67 nu = 0.217435 obj = -33.244689, rho = 0.080531 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 62 nu = 0.182991 obj = -39.251454, rho = 0.135482 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 148 nu = 0.148497 obj = -46.106324, rho = 0.156924 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *..* optimization finished, #iter = 241 nu = 0.124242 obj = -53.487895, rho = 0.068029 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 159 nu = 0.100503 obj = -61.345284, rho = 0.071124 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -0.803569, rho = 0.938204 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -1.145532, rho = 0.911109 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -1.626349, rho = 0.872135 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -2.295056, rho = 0.816073 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -3.209525, rho = 0.735430 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -4.426797, rho = 0.619429 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -5.974707, rho = 0.452568 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 69% (69/100) (classification) Accuracy = 61.3% (613/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -7.781111, rho = 0.212853 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 93% (93/100) (classification) Accuracy = 89.8% (898/1000) (classification) * optimization finished, #iter = 43 nu = 0.754823 obj = -9.701675, rho = 0.059559 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 36 nu = 0.662866 obj = -11.827359, rho = 0.006153 nSV = 69, nBSV = 66 Total nSV = 69 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 36 nu = 0.563747 obj = -14.195664, rho = 0.027531 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 55 nu = 0.471665 obj = -16.920709, rho = 0.032691 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 54 nu = 0.386655 obj = -20.181142, rho = -0.016916 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 60 nu = 0.330349 obj = -23.933312, rho = -0.038806 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 51 nu = 0.267060 obj = -28.203906, rho = -0.053203 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 54 nu = 0.219981 obj = -33.136901, rho = -0.092084 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 42 nu = 0.182486 obj = -39.173295, rho = -0.115364 nSV = 21, nBSV = 17 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.151672 obj = -45.191036, rho = -0.056690 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 171 nu = 0.121458 obj = -51.731806, rho = -0.026856 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 166 nu = 0.097395 obj = -59.190540, rho = 0.040961 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 36 nu = 0.700000 obj = -0.686217, rho = -0.963661 nSV = 71, nBSV = 69 Total nSV = 71 Accuracy = 65% (65/100) (classification) Accuracy = 53.4% (534/1000) (classification) * optimization finished, #iter = 36 nu = 0.700000 obj = -0.978397, rho = -0.947728 nSV = 71, nBSV = 69 Total nSV = 71 Accuracy = 65% (65/100) (classification) Accuracy = 53.4% (534/1000) (classification) * optimization finished, #iter = 36 nu = 0.700000 obj = -1.389389, rho = -0.924809 nSV = 71, nBSV = 69 Total nSV = 71 Accuracy = 65% (65/100) (classification) Accuracy = 53.4% (534/1000) (classification) * optimization finished, #iter = 36 nu = 0.700000 obj = -1.961351, rho = -0.891841 nSV = 71, nBSV = 69 Total nSV = 71 Accuracy = 65% (65/100) (classification) Accuracy = 53.4% (534/1000) (classification) * optimization finished, #iter = 36 nu = 0.700000 obj = -2.744300, rho = -0.844419 nSV = 71, nBSV = 69 Total nSV = 71 Accuracy = 65% (65/100) (classification) Accuracy = 53.4% (534/1000) (classification) * optimization finished, #iter = 36 nu = 0.700000 obj = -3.788206, rho = -0.776204 nSV = 71, nBSV = 69 Total nSV = 71 Accuracy = 65% (65/100) (classification) Accuracy = 53.4% (534/1000) (classification) * optimization finished, #iter = 36 nu = 0.700000 obj = -5.119464, rho = -0.678081 nSV = 71, nBSV = 69 Total nSV = 71 Accuracy = 74% (74/100) (classification) Accuracy = 60.8% (608/1000) (classification) * optimization finished, #iter = 36 nu = 0.700000 obj = -6.681939, rho = -0.536936 nSV = 71, nBSV = 69 Total nSV = 71 Accuracy = 93% (93/100) (classification) Accuracy = 82.1% (821/1000) (classification) * optimization finished, #iter = 35 nu = 0.660000 obj = -8.316840, rho = -0.414927 nSV = 67, nBSV = 65 Total nSV = 67 Accuracy = 95% (95/100) (classification) Accuracy = 91.8% (918/1000) (classification) * optimization finished, #iter = 35 nu = 0.567801 obj = -10.051967, rho = -0.407124 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 96% (96/100) (classification) Accuracy = 93.9% (939/1000) (classification) * optimization finished, #iter = 53 nu = 0.487201 obj = -11.995091, rho = -0.375273 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 97% (97/100) (classification) Accuracy = 94.7% (947/1000) (classification) * optimization finished, #iter = 37 nu = 0.402753 obj = -14.240096, rho = -0.381905 nSV = 43, nBSV = 40 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 95.1% (951/1000) (classification) * optimization finished, #iter = 42 nu = 0.328856 obj = -16.737444, rho = -0.373269 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 63 nu = 0.267015 obj = -19.879666, rho = -0.328662 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 54 nu = 0.219830 obj = -23.837147, rho = -0.300441 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 70 nu = 0.185950 obj = -28.524437, rho = -0.275157 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 38 nu = 0.154780 obj = -33.958066, rho = -0.243583 nSV = 18, nBSV = 13 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *..* optimization finished, #iter = 291 nu = 0.132504 obj = -39.178920, rho = -0.069191 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*..* optimization finished, #iter = 353 nu = 0.106097 obj = -44.821538, rho = -0.078863 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..*.* optimization finished, #iter = 304 nu = 0.089904 obj = -49.764712, rho = -0.130461 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -0.819740, rho = -0.937616 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -1.166377, rho = -0.910264 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -1.651337, rho = -0.870920 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -2.320659, rho = -0.814324 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -3.224959, rho = -0.732915 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -4.404729, rho = -0.615812 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 62% (62/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -5.851363, rho = -0.447364 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 87% (87/100) (classification) Accuracy = 77.6% (776/1000) (classification) * optimization finished, #iter = 46 nu = 0.804868 obj = -7.455035, rho = -0.294585 nSV = 82, nBSV = 79 Total nSV = 82 Accuracy = 97% (97/100) (classification) Accuracy = 92.5% (925/1000) (classification) * optimization finished, #iter = 41 nu = 0.721549 obj = -9.235290, rho = -0.264343 nSV = 74, nBSV = 71 Total nSV = 74 Accuracy = 97% (97/100) (classification) Accuracy = 93.6% (936/1000) (classification) * optimization finished, #iter = 43 nu = 0.636792 obj = -11.252432, rho = -0.171509 nSV = 65, nBSV = 62 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 36 nu = 0.540827 obj = -13.502114, rho = -0.229432 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 40 nu = 0.454564 obj = -16.028833, rho = -0.253821 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 52 nu = 0.372336 obj = -18.856097, rho = -0.274700 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 71 nu = 0.302246 obj = -22.202912, rho = -0.236187 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 69 nu = 0.249135 obj = -26.319895, rho = -0.199652 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 99 nu = 0.201074 obj = -31.449070, rho = -0.173770 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.171855 obj = -37.689466, rho = -0.084537 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 75 nu = 0.141971 obj = -44.745638, rho = -0.029824 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 159 nu = 0.121355 obj = -52.072978, rho = -0.163244 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 82 nu = 0.097468 obj = -59.692771, rho = -0.218616 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -0.881266, rho = -0.932485 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -1.255842, rho = -0.902883 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -1.782019, rho = -0.860301 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -2.512761, rho = -0.799051 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -3.509815, rho = -0.710944 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -4.832124, rho = -0.584208 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 56% (56/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -6.502659, rho = -0.401904 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 75% (75/100) (classification) Accuracy = 78.6% (786/1000) (classification) * optimization finished, #iter = 48 nu = 0.882416 obj = -8.435250, rho = -0.236015 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 99% (99/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 44 nu = 0.806865 obj = -10.626137, rho = -0.127283 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 39 nu = 0.712611 obj = -13.061613, rho = -0.146737 nSV = 73, nBSV = 70 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 51 nu = 0.613102 obj = -15.864740, rho = -0.212143 nSV = 66, nBSV = 59 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 35 nu = 0.526143 obj = -19.103555, rho = -0.133219 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 45 nu = 0.438645 obj = -22.788005, rho = -0.104381 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 39 nu = 0.360299 obj = -27.308806, rho = -0.150986 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 48 nu = 0.309715 obj = -32.724925, rho = -0.160620 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 76 nu = 0.253542 obj = -38.740412, rho = -0.118384 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 61 nu = 0.208965 obj = -46.143088, rho = -0.047614 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.170199 obj = -54.966258, rho = 0.018021 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 74 nu = 0.139388 obj = -66.515549, rho = 0.020433 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.114587 obj = -81.676689, rho = 0.070515 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -0.916067, rho = -0.920243 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 52.3% (523/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.302622, rho = -0.885273 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 52.3% (523/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.842523, rho = -0.834971 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 52.3% (523/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -2.585753, rho = -0.762614 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 52.3% (523/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -3.585760, rho = -0.658532 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 52.4% (524/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.881257, rho = -0.508816 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 72% (72/100) (classification) Accuracy = 72.4% (724/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.448959, rho = -0.293456 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 92% (92/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 48 nu = 0.874925 obj = -8.199807, rho = -0.219371 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 43 nu = 0.787304 obj = -10.259693, rho = -0.175374 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 64 nu = 0.700000 obj = -12.568835, rho = -0.108199 nSV = 73, nBSV = 66 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 62 nu = 0.593222 obj = -15.178238, rho = -0.042803 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 66 nu = 0.502929 obj = -18.246462, rho = -0.103670 nSV = 54, nBSV = 46 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 56 nu = 0.425477 obj = -21.767492, rho = -0.069004 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 41 nu = 0.353700 obj = -25.573000, rho = -0.048349 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.290334 obj = -29.938589, rho = -0.057755 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.234371 obj = -34.786931, rho = -0.034508 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 62 nu = 0.187821 obj = -40.647379, rho = -0.002859 nSV = 25, nBSV = 15 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 50 nu = 0.158431 obj = -47.521085, rho = -0.114197 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 83 nu = 0.126109 obj = -54.819069, rho = -0.102673 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.103200 obj = -63.570584, rho = -0.180477 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 38 nu = 0.660000 obj = -0.651250, rho = 0.966582 nSV = 66, nBSV = 66 Total nSV = 66 Accuracy = 67% (67/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 38 nu = 0.660000 obj = -0.931273, rho = 0.951930 nSV = 66, nBSV = 66 Total nSV = 66 Accuracy = 67% (67/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 38 nu = 0.660000 obj = -1.328171, rho = 0.930854 nSV = 66, nBSV = 66 Total nSV = 66 Accuracy = 67% (67/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 38 nu = 0.660000 obj = -1.886882, rho = 0.900536 nSV = 66, nBSV = 66 Total nSV = 66 Accuracy = 67% (67/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 40 nu = 0.660000 obj = -2.665301, rho = 0.856927 nSV = 66, nBSV = 66 Total nSV = 66 Accuracy = 67% (67/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 39 nu = 0.660000 obj = -3.732753, rho = 0.794196 nSV = 66, nBSV = 66 Total nSV = 66 Accuracy = 67% (67/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 39 nu = 0.660000 obj = -5.160087, rho = 0.703962 nSV = 66, nBSV = 66 Total nSV = 66 Accuracy = 67% (67/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 37 nu = 0.660000 obj = -6.989475, rho = 0.574163 nSV = 66, nBSV = 66 Total nSV = 66 Accuracy = 69% (69/100) (classification) Accuracy = 57.3% (573/1000) (classification) * optimization finished, #iter = 39 nu = 0.660000 obj = -9.157966, rho = 0.383330 nSV = 67, nBSV = 66 Total nSV = 67 Accuracy = 89% (89/100) (classification) Accuracy = 80.2% (802/1000) (classification) * optimization finished, #iter = 40 nu = 0.640000 obj = -11.474589, rho = 0.180104 nSV = 66, nBSV = 63 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 94% (940/1000) (classification) * optimization finished, #iter = 47 nu = 0.545564 obj = -13.814562, rho = 0.117210 nSV = 56, nBSV = 53 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 37 nu = 0.457024 obj = -16.496516, rho = 0.072091 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 62 nu = 0.377890 obj = -19.598306, rho = -0.010165 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 74 nu = 0.310620 obj = -23.487154, rho = 0.012745 nSV = 35, nBSV = 26 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 54 nu = 0.265135 obj = -28.277836, rho = -0.034782 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 55 nu = 0.216446 obj = -33.794988, rho = 0.010603 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 155 nu = 0.180121 obj = -40.395738, rho = 0.041860 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.152824 obj = -48.367925, rho = 0.113887 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 80 nu = 0.122539 obj = -58.111119, rho = 0.128333 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 42 nu = 0.106239 obj = -70.833154, rho = 0.253776 nSV = 14, nBSV = 9 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.964645, rho = -0.052644 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 89% (89/100) (classification) Accuracy = 88.7% (887/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.365296, rho = -0.075725 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 89% (89/100) (classification) Accuracy = 88.7% (887/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.917772, rho = -0.108927 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 89% (89/100) (classification) Accuracy = 88.7% (887/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.663154, rho = -0.156686 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 89% (89/100) (classification) Accuracy = 88.7% (887/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.633284, rho = -0.225385 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 89% (89/100) (classification) Accuracy = 88.7% (887/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -4.817582, rho = -0.324205 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 89% (89/100) (classification) Accuracy = 88.7% (887/1000) (classification) * optimization finished, #iter = 47 nu = 0.937132 obj = -6.200093, rho = -0.313418 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 91% (91/100) (classification) Accuracy = 89.6% (896/1000) (classification) * optimization finished, #iter = 46 nu = 0.838428 obj = -7.862638, rho = -0.275696 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 92% (92/100) (classification) Accuracy = 92% (920/1000) (classification) * optimization finished, #iter = 39 nu = 0.742669 obj = -9.921454, rho = -0.257735 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 94% (94/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 41 nu = 0.662340 obj = -12.369333, rho = -0.271442 nSV = 68, nBSV = 65 Total nSV = 68 Accuracy = 96% (96/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 39 nu = 0.578911 obj = -15.215475, rho = -0.185767 nSV = 62, nBSV = 55 Total nSV = 62 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 52 nu = 0.486157 obj = -18.691403, rho = -0.186914 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 37 nu = 0.428085 obj = -22.897581, rho = -0.152808 nSV = 44, nBSV = 41 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 54 nu = 0.360971 obj = -27.627050, rho = -0.223404 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 44 nu = 0.302384 obj = -33.383142, rho = -0.284714 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.266666 obj = -40.061092, rho = -0.247386 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 86 nu = 0.219093 obj = -46.683143, rho = -0.206481 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *........* optimization finished, #iter = 819 nu = 0.175942 obj = -54.566143, rho = -0.248472 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 277 nu = 0.138443 obj = -64.982917, rho = -0.250772 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.119361 obj = -78.576084, rho = -0.229374 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -0.862516, rho = 0.934093 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -1.229658, rho = 0.905196 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -1.745985, rho = 0.863629 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -2.464301, rho = 0.803837 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -3.447087, rho = 0.717829 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -4.756335, rho = 0.594111 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -6.423522, rho = 0.416149 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 76% (76/100) (classification) Accuracy = 70.1% (701/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -8.374544, rho = 0.160160 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 91% (91/100) (classification) Accuracy = 92.9% (929/1000) (classification) * optimization finished, #iter = 58 nu = 0.787093 obj = -10.585117, rho = 0.084458 nSV = 82, nBSV = 76 Total nSV = 82 Accuracy = 96% (96/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 48 nu = 0.708965 obj = -13.247773, rho = 0.109217 nSV = 73, nBSV = 68 Total nSV = 73 Accuracy = 96% (96/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 41 nu = 0.612544 obj = -16.407116, rho = 0.129339 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 34 nu = 0.535109 obj = -20.216194, rho = 0.090240 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 42 nu = 0.454593 obj = -24.736865, rho = 0.031843 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 45 nu = 0.381593 obj = -30.405595, rho = 0.003865 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 56 nu = 0.328439 obj = -37.544312, rho = -0.114737 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 88 nu = 0.281055 obj = -46.118170, rho = -0.077263 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 61 nu = 0.253054 obj = -56.182803, rho = 0.025331 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *....* optimization finished, #iter = 431 nu = 0.211673 obj = -66.554030, rho = 0.064524 nSV = 29, nBSV = 17 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 69 nu = 0.179239 obj = -77.956279, rho = 0.066815 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 98 nu = 0.143387 obj = -91.021012, rho = 0.110863 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -0.913712, rho = 0.876382 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.297750, rho = 0.822181 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.832443, rho = 0.744217 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -2.564896, rho = 0.632069 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -3.542604, rho = 0.470749 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 54% (54/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.791961, rho = 0.238700 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 78% (78/100) (classification) Accuracy = 72.4% (724/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -6.271734, rho = -0.011408 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 92% (92/100) (classification) Accuracy = 93.2% (932/1000) (classification) * optimization finished, #iter = 46 nu = 0.862612 obj = -7.928346, rho = -0.116376 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 45 nu = 0.773123 obj = -9.828371, rho = -0.012316 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 38 nu = 0.671715 obj = -11.951468, rho = -0.012234 nSV = 69, nBSV = 64 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 57 nu = 0.552980 obj = -14.555358, rho = -0.058473 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 33 nu = 0.469487 obj = -17.838406, rho = -0.056602 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 37 nu = 0.407389 obj = -21.651681, rho = -0.159550 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 47 nu = 0.352002 obj = -25.895614, rho = -0.247642 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 84 nu = 0.293790 obj = -30.586290, rho = -0.183389 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 71 nu = 0.245460 obj = -35.788763, rho = -0.229197 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 94 nu = 0.197211 obj = -41.207779, rho = -0.200265 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.160139 obj = -47.233664, rho = -0.299833 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 80 nu = 0.128229 obj = -53.541272, rho = -0.293978 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .*..* optimization finished, #iter = 328 nu = 0.103530 obj = -59.366563, rho = -0.372592 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -0.912488, rho = 0.871913 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.4% (494/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -1.295218, rho = 0.815753 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.4% (494/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -1.827204, rho = 0.734970 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.4% (494/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -2.554055, rho = 0.618767 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.4% (494/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -3.520172, rho = 0.451616 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -4.745547, rho = 0.211177 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 84% (84/100) (classification) Accuracy = 76.9% (769/1000) (classification) * optimization finished, #iter = 47 nu = 0.934950 obj = -6.168617, rho = -0.101915 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 97% (97/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -7.795607, rho = -0.080079 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 97% (97/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 43 nu = 0.752350 obj = -9.653866, rho = -0.093129 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 37 nu = 0.660000 obj = -11.790545, rho = -0.136314 nSV = 67, nBSV = 64 Total nSV = 67 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 32 nu = 0.560000 obj = -14.146410, rho = -0.188229 nSV = 57, nBSV = 55 Total nSV = 57 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 54 nu = 0.468226 obj = -16.907874, rho = -0.162599 nSV = 49, nBSV = 42 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 59 nu = 0.391944 obj = -20.158167, rho = -0.108555 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 51 nu = 0.316913 obj = -24.117231, rho = -0.096179 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 75 nu = 0.268942 obj = -29.101824, rho = -0.104203 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 62 nu = 0.219040 obj = -35.257572, rho = -0.137612 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 73 nu = 0.184118 obj = -43.033053, rho = -0.177893 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 91 nu = 0.156327 obj = -52.829222, rho = -0.119518 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 92 nu = 0.132439 obj = -65.214803, rho = -0.176397 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 143 nu = 0.118275 obj = -79.710445, rho = -0.182235 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 48 nu = 0.880000 obj = -0.862313, rho = 0.914498 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 48 nu = 0.880000 obj = -1.229240, rho = 0.877010 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 48 nu = 0.880000 obj = -1.745119, rho = 0.823085 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 48 nu = 0.880000 obj = -2.462509, rho = 0.745516 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 48 nu = 0.880000 obj = -3.443380, rho = 0.633938 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -4.748665, rho = 0.473438 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 57% (57/100) (classification) Accuracy = 52.5% (525/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -6.407651, rho = 0.242567 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 83% (83/100) (classification) Accuracy = 73.1% (731/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -8.383143, rho = 0.028260 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 92% (92/100) (classification) Accuracy = 90.6% (906/1000) (classification) * optimization finished, #iter = 51 nu = 0.791138 obj = -10.638425, rho = -0.085112 nSV = 81, nBSV = 77 Total nSV = 81 Accuracy = 94% (94/100) (classification) Accuracy = 94.5% (945/1000) (classification) * optimization finished, #iter = 42 nu = 0.700339 obj = -13.370267, rho = -0.065498 nSV = 72, nBSV = 67 Total nSV = 72 Accuracy = 95% (95/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 51 nu = 0.601896 obj = -16.782636, rho = -0.085758 nSV = 65, nBSV = 58 Total nSV = 65 Accuracy = 95% (95/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 43 nu = 0.529886 obj = -21.098414, rho = -0.011208 nSV = 57, nBSV = 51 Total nSV = 57 Accuracy = 96% (96/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 64 nu = 0.474818 obj = -26.250366, rho = 0.058010 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 96% (96/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 81 nu = 0.403348 obj = -32.583857, rho = -0.000922 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 51 nu = 0.348434 obj = -40.690803, rho = 0.043458 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 43 nu = 0.300532 obj = -50.871344, rho = 0.097235 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 73 nu = 0.261204 obj = -63.712135, rho = 0.084446 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) *..*.* optimization finished, #iter = 289 nu = 0.225540 obj = -80.223637, rho = 0.049265 nSV = 28, nBSV = 18 Total nSV = 28 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 86 nu = 0.198094 obj = -101.359890, rho = 0.078230 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 178 nu = 0.174535 obj = -127.356698, rho = 0.260139 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -0.860856, rho = 0.911041 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 52.7% (527/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -1.226223, rho = 0.872037 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 52.7% (527/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -1.738878, rho = 0.815932 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 52.7% (527/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -2.449595, rho = 0.735227 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 52.7% (527/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -3.416658, rho = 0.619137 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 52.7% (527/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -4.693373, rho = 0.452148 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 59% (59/100) (classification) Accuracy = 55.1% (551/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -6.293246, rho = 0.211943 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 82% (82/100) (classification) Accuracy = 79.1% (791/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -8.128469, rho = -0.025382 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 92% (92/100) (classification) Accuracy = 94.2% (942/1000) (classification) * optimization finished, #iter = 51 nu = 0.775853 obj = -10.206500, rho = -0.031296 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 94% (94/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 39 nu = 0.675098 obj = -12.654698, rho = -0.014701 nSV = 69, nBSV = 66 Total nSV = 69 Accuracy = 95% (95/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 35 nu = 0.600000 obj = -15.585335, rho = -0.001763 nSV = 61, nBSV = 58 Total nSV = 61 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 44 nu = 0.503620 obj = -19.014833, rho = 0.025566 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 53 nu = 0.421739 obj = -23.387915, rho = 0.024361 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 43 nu = 0.366313 obj = -28.817484, rho = -0.087851 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 71 nu = 0.315080 obj = -35.212806, rho = -0.017573 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 38 nu = 0.264364 obj = -43.168006, rho = -0.082025 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 51 nu = 0.228136 obj = -52.708797, rho = -0.016766 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 85 nu = 0.193096 obj = -64.224454, rho = 0.052986 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.162181 obj = -78.674111, rho = -0.022026 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 178 nu = 0.141488 obj = -95.562183, rho = -0.171053 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -0.916542, rho = -0.920411 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.303605, rho = -0.885515 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.844559, rho = -0.835319 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.589965, rho = -0.763115 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.594474, rho = -0.659253 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -4.899288, rho = -0.509852 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 64% (64/100) (classification) Accuracy = 68.2% (682/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -6.486267, rho = -0.294947 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 97% (97/100) (classification) Accuracy = 93.7% (937/1000) (classification) * optimization finished, #iter = 45 nu = 0.883924 obj = -8.282619, rho = -0.169954 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 41 nu = 0.800656 obj = -10.348431, rho = -0.124739 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.701919 obj = -12.683787, rho = -0.059009 nSV = 74, nBSV = 68 Total nSV = 74 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 37 nu = 0.595617 obj = -15.412419, rho = -0.125129 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 26 nu = 0.500000 obj = -18.799046, rho = -0.088764 nSV = 51, nBSV = 49 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 33 nu = 0.435494 obj = -22.639350, rho = -0.058950 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 41 nu = 0.370129 obj = -26.700618, rho = -0.102314 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 64 nu = 0.303539 obj = -31.276770, rho = -0.147573 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.243472 obj = -36.524994, rho = -0.143722 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 73 nu = 0.199435 obj = -42.843320, rho = -0.206829 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.159533 obj = -50.718180, rho = -0.208161 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 136 nu = 0.131726 obj = -60.658312, rho = -0.262022 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 81 nu = 0.110480 obj = -71.978061, rho = -0.331392 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.949041, rho = -0.909639 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.345623, rho = -0.870020 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.895210, rho = -0.813031 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.642570, rho = -0.731054 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.628235, rho = -0.613134 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 56% (56/100) (classification) Accuracy = 57.3% (573/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.861138, rho = -0.443513 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 89% (89/100) (classification) Accuracy = 85% (850/1000) (classification) * optimization finished, #iter = 48 nu = 0.948308 obj = -6.269508, rho = -0.302152 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 93% (93/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 48 nu = 0.871785 obj = -7.902263, rho = -0.225541 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 95% (95/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 44 nu = 0.773759 obj = -9.751544, rho = -0.183514 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 96% (96/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 43 nu = 0.664770 obj = -11.857291, rho = -0.222346 nSV = 68, nBSV = 64 Total nSV = 68 Accuracy = 96% (96/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 36 nu = 0.558055 obj = -14.359498, rho = -0.203976 nSV = 57, nBSV = 54 Total nSV = 57 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 38 nu = 0.471007 obj = -17.308406, rho = -0.195258 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 95 nu = 0.392920 obj = -20.857304, rho = -0.203908 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.322645 obj = -25.397963, rho = -0.239619 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 96% (96/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.268680 obj = -31.470935, rho = -0.251087 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 96% (96/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.226649 obj = -39.612214, rho = -0.289162 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 96% (96/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 40 nu = 0.200010 obj = -50.614379, rho = -0.346524 nSV = 22, nBSV = 17 Total nSV = 22 Accuracy = 96% (96/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 52 nu = 0.183122 obj = -64.099521, rho = -0.376602 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.161267 obj = -79.632905, rho = -0.429158 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.136452 obj = -99.298435, rho = -0.480724 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -0.912710, rho = -0.933562 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -1.295676, rho = -0.904432 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -1.828153, rho = -0.862531 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -2.556018, rho = -0.802257 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -3.524234, rho = -0.715557 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -4.753952, rho = -0.590843 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 75% (75/100) (classification) Accuracy = 69.9% (699/1000) (classification) * optimization finished, #iter = 50 nu = 0.931568 obj = -6.186314, rho = -0.422001 nSV = 95, nBSV = 92 Total nSV = 95 Accuracy = 95% (95/100) (classification) Accuracy = 90.9% (909/1000) (classification) * optimization finished, #iter = 49 nu = 0.848177 obj = -7.810882, rho = -0.377861 nSV = 86, nBSV = 82 Total nSV = 86 Accuracy = 97% (97/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 42 nu = 0.760000 obj = -9.665439, rho = -0.280558 nSV = 77, nBSV = 75 Total nSV = 77 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 46 nu = 0.661781 obj = -11.699389, rho = -0.304039 nSV = 69, nBSV = 63 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 37 nu = 0.549402 obj = -14.108159, rho = -0.296687 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.459451 obj = -17.037562, rho = -0.324661 nSV = 48, nBSV = 44 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 33 nu = 0.393451 obj = -20.571108, rho = -0.369668 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 37 nu = 0.335611 obj = -24.406299, rho = -0.447078 nSV = 36, nBSV = 32 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 87 nu = 0.275216 obj = -28.613360, rho = -0.463258 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 51 nu = 0.221997 obj = -33.710332, rho = -0.404174 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 68 nu = 0.182473 obj = -39.750379, rho = -0.448509 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.146917 obj = -47.165021, rho = -0.447237 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 140 nu = 0.122713 obj = -56.387536, rho = -0.544446 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 88 nu = 0.102491 obj = -67.169962, rho = -0.729963 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.968772, rho = -0.023292 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.373834, rho = -0.033504 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.935439, rho = -0.048194 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.699709, rho = -0.069324 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.708920, rho = -0.099719 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -4.974083, rho = -0.143441 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 48 nu = 0.958488 obj = -6.445045, rho = -0.135427 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 49 nu = 0.881459 obj = -8.210668, rho = -0.087737 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 45 nu = 0.803267 obj = -10.225425, rho = 0.004374 nSV = 83, nBSV = 79 Total nSV = 83 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 42 nu = 0.718173 obj = -12.293468, rho = -0.087364 nSV = 73, nBSV = 69 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 39 nu = 0.598324 obj = -14.465158, rho = -0.076209 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 71 nu = 0.486001 obj = -16.855826, rho = -0.102111 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 64 nu = 0.387272 obj = -19.849539, rho = -0.129510 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 70 nu = 0.318799 obj = -23.604921, rho = -0.078279 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.265082 obj = -27.790721, rho = 0.018761 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.216152 obj = -32.993521, rho = 0.073977 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 77 nu = 0.176545 obj = -39.160856, rho = 0.129931 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 49 nu = 0.145137 obj = -46.789007, rho = 0.105898 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 69 nu = 0.120579 obj = -56.276760, rho = 0.047592 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 96 nu = 0.098828 obj = -67.766984, rho = 0.040026 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -0.840257, rho = -0.933633 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -1.196216, rho = -0.904535 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -1.694932, rho = -0.862678 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -2.384766, rho = -0.802470 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -3.320061, rho = -0.715863 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -4.547505, rho = -0.591283 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 61% (61/100) (classification) Accuracy = 54% (540/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -6.069105, rho = -0.412081 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 84% (84/100) (classification) Accuracy = 82.6% (826/1000) (classification) * optimization finished, #iter = 43 nu = 0.820000 obj = -7.829604, rho = -0.269150 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 94% (94/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 43 nu = 0.760000 obj = -9.815799, rho = -0.184335 nSV = 77, nBSV = 74 Total nSV = 77 Accuracy = 95% (95/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 38 nu = 0.662352 obj = -12.000960, rho = -0.116359 nSV = 68, nBSV = 66 Total nSV = 68 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.563790 obj = -14.537260, rho = -0.071007 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 35 nu = 0.475330 obj = -17.714455, rho = -0.045613 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 46 nu = 0.404937 obj = -21.386485, rho = 0.001457 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 68 nu = 0.338452 obj = -25.887871, rho = -0.052064 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 185 nu = 0.274658 obj = -31.709405, rho = -0.069789 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 59 nu = 0.233793 obj = -39.681489, rho = -0.038541 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 50 nu = 0.203998 obj = -49.859746, rho = -0.106188 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 56 nu = 0.182213 obj = -61.684893, rho = -0.195971 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 79 nu = 0.154415 obj = -75.726146, rho = -0.193376 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 149 nu = 0.132608 obj = -93.039828, rho = -0.154719 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -0.952485, rho = -0.901899 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 53.4% (534/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.352750, rho = -0.858886 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 53.4% (534/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.909957, rho = -0.797015 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 53.4% (534/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -2.673083, rho = -0.708016 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 53.4% (534/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -3.691370, rho = -0.579996 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 53% (53/100) (classification) Accuracy = 54.7% (547/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -4.991773, rho = -0.395845 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 84% (84/100) (classification) Accuracy = 80.5% (805/1000) (classification) * optimization finished, #iter = 52 nu = 0.960000 obj = -6.528767, rho = -0.168470 nSV = 98, nBSV = 95 Total nSV = 98 Accuracy = 97% (97/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -8.374214, rho = -0.110436 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 96% (96/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 45 nu = 0.807026 obj = -10.481261, rho = -0.025086 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 97% (97/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 41 nu = 0.717518 obj = -12.912863, rho = -0.079319 nSV = 74, nBSV = 70 Total nSV = 74 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 89 nu = 0.613252 obj = -15.545881, rho = -0.098690 nSV = 65, nBSV = 57 Total nSV = 65 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 79 nu = 0.510017 obj = -18.743453, rho = -0.062293 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 66 nu = 0.422440 obj = -22.598848, rho = -0.061437 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 69 nu = 0.353282 obj = -27.528047, rho = -0.077933 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 45 nu = 0.311426 obj = -33.412898, rho = -0.022344 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 77 nu = 0.251728 obj = -40.212298, rho = 0.007292 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.212745 obj = -48.632278, rho = 0.061166 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 51 nu = 0.178981 obj = -59.262800, rho = 0.005536 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.150684 obj = -72.048568, rho = 0.037263 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.125174 obj = -88.631888, rho = 0.067142 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.967602, rho = -0.049715 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 92% (92/100) (classification) Accuracy = 90.5% (905/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.371415, rho = -0.071513 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 92% (92/100) (classification) Accuracy = 90.5% (905/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.930433, rho = -0.102868 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 92% (92/100) (classification) Accuracy = 90.5% (905/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.689352, rho = -0.147971 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 92% (92/100) (classification) Accuracy = 90.5% (905/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.687491, rho = -0.212848 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 92% (92/100) (classification) Accuracy = 90.5% (905/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -4.929743, rho = -0.306172 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 92% (92/100) (classification) Accuracy = 90.5% (905/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -6.377065, rho = -0.339940 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 92% (92/100) (classification) Accuracy = 92.5% (925/1000) (classification) * optimization finished, #iter = 48 nu = 0.868418 obj = -8.060655, rho = -0.292871 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 96% (96/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 41 nu = 0.780000 obj = -10.024482, rho = -0.306355 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 40 nu = 0.665617 obj = -12.332325, rho = -0.350494 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 47 nu = 0.578299 obj = -15.197470, rho = -0.275803 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 63 nu = 0.499880 obj = -18.447795, rho = -0.315952 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 33 nu = 0.421133 obj = -22.256269, rho = -0.264523 nSV = 45, nBSV = 41 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 79 nu = 0.351012 obj = -26.749256, rho = -0.295766 nSV = 39, nBSV = 30 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.299528 obj = -32.380057, rho = -0.250898 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 51 nu = 0.252284 obj = -38.510696, rho = -0.191580 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 46 nu = 0.206312 obj = -45.697422, rho = -0.235609 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.171691 obj = -54.209303, rho = -0.293687 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 139 nu = 0.142991 obj = -64.128270, rho = -0.263649 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.119388 obj = -74.856586, rho = -0.197395 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.840000 obj = -0.823710, rho = -0.961843 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 45 nu = 0.840000 obj = -1.174594, rho = -0.945020 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 45 nu = 0.840000 obj = -1.668338, rho = -0.920915 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 47 nu = 0.840000 obj = -2.355844, rho = -0.886101 nSV = 86, nBSV = 81 Total nSV = 86 Accuracy = 58% (58/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 48 nu = 0.840000 obj = -3.297763, rho = -0.835692 nSV = 86, nBSV = 81 Total nSV = 86 Accuracy = 58% (58/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 50 nu = 0.840000 obj = -4.555377, rho = -0.763678 nSV = 86, nBSV = 81 Total nSV = 86 Accuracy = 58% (58/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 48 nu = 0.840000 obj = -6.163076, rho = -0.660062 nSV = 86, nBSV = 82 Total nSV = 86 Accuracy = 75% (75/100) (classification) Accuracy = 66.3% (663/1000) (classification) * optimization finished, #iter = 47 nu = 0.840000 obj = -8.059129, rho = -0.510412 nSV = 86, nBSV = 82 Total nSV = 86 Accuracy = 94% (94/100) (classification) Accuracy = 89.8% (898/1000) (classification) * optimization finished, #iter = 43 nu = 0.760676 obj = -10.165160, rho = -0.409638 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 96% (96/100) (classification) Accuracy = 94.6% (946/1000) (classification) * optimization finished, #iter = 61 nu = 0.685137 obj = -12.639344, rho = -0.306415 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 56 nu = 0.600590 obj = -15.452261, rho = -0.313752 nSV = 63, nBSV = 57 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 46 nu = 0.508850 obj = -18.641063, rho = -0.355580 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 41 nu = 0.425991 obj = -22.478880, rho = -0.364241 nSV = 45, nBSV = 42 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 64 nu = 0.353440 obj = -27.117056, rho = -0.359528 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 75 nu = 0.297904 obj = -32.883663, rho = -0.367187 nSV = 34, nBSV = 25 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 65 nu = 0.252415 obj = -39.861832, rho = -0.452827 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.216136 obj = -48.112910, rho = -0.335832 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.177492 obj = -57.155714, rho = -0.274530 nSV = 23, nBSV = 12 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) ..* optimization finished, #iter = 263 nu = 0.143623 obj = -69.353523, rho = -0.270318 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 83 nu = 0.121512 obj = -85.342489, rho = -0.214701 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -0.918021, rho = -0.926148 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 51.1% (511/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.306665, rho = -0.893768 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 51.1% (511/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.850889, rho = -0.847191 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 51.1% (511/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.603063, rho = -0.780191 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 51.1% (511/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.621576, rho = -0.683816 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 51.1% (511/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -4.955366, rho = -0.545185 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 63% (63/100) (classification) Accuracy = 60.8% (608/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -6.602300, rho = -0.345772 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 88% (88/100) (classification) Accuracy = 90.6% (906/1000) (classification) * optimization finished, #iter = 45 nu = 0.888803 obj = -8.503850, rho = -0.282983 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 90% (90/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 44 nu = 0.810305 obj = -10.776998, rho = -0.221680 nSV = 82, nBSV = 79 Total nSV = 82 Accuracy = 95% (95/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 49 nu = 0.728498 obj = -13.351439, rho = -0.129273 nSV = 75, nBSV = 70 Total nSV = 75 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 93 nu = 0.626347 obj = -16.297489, rho = -0.079758 nSV = 67, nBSV = 60 Total nSV = 67 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 46 nu = 0.528430 obj = -19.825965, rho = -0.069101 nSV = 57, nBSV = 50 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 48 nu = 0.446484 obj = -24.281658, rho = -0.103884 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 45 nu = 0.371582 obj = -30.053938, rho = -0.068253 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 58 nu = 0.320402 obj = -37.463764, rho = -0.023222 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 68 nu = 0.278747 obj = -46.801899, rho = 0.050474 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 66 nu = 0.237189 obj = -58.982964, rho = 0.013170 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 49 nu = 0.205341 obj = -74.835264, rho = -0.149627 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.184111 obj = -95.458396, rho = -0.210691 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 96% (96/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 72 nu = 0.162192 obj = -121.390236, rho = -0.221028 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 96% (96/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -0.878085, rho = -0.925998 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -1.249260, rho = -0.893551 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -1.768400, rho = -0.846879 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -2.484581, rho = -0.779743 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -3.451506, rho = -0.683171 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -4.711474, rho = -0.544325 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 61% (61/100) (classification) Accuracy = 54.6% (546/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -6.253019, rho = -0.344438 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 85% (85/100) (classification) Accuracy = 88.8% (888/1000) (classification) * optimization finished, #iter = 47 nu = 0.864085 obj = -7.957560, rho = -0.196496 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 93% (93/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 44 nu = 0.761939 obj = -9.891502, rho = -0.127570 nSV = 79, nBSV = 74 Total nSV = 79 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 41 nu = 0.667382 obj = -12.125340, rho = -0.081785 nSV = 69, nBSV = 64 Total nSV = 69 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 40 nu = 0.575030 obj = -14.802470, rho = -0.058120 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 29 nu = 0.490301 obj = -17.849936, rho = 0.035763 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 140 nu = 0.417550 obj = -21.386728, rho = 0.083165 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 67 nu = 0.351179 obj = -25.252565, rho = 0.073933 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 69 nu = 0.280801 obj = -29.526098, rho = 0.052502 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.231765 obj = -34.813901, rho = 0.110721 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.188863 obj = -40.873808, rho = 0.100297 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.152338 obj = -48.239242, rho = 0.144665 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 61 nu = 0.126128 obj = -57.180116, rho = 0.142868 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 44 nu = 0.103398 obj = -68.258616, rho = 0.241350 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -0.863177, rho = -0.941382 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -1.231027, rho = -0.915681 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -1.748816, rho = -0.878712 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -2.470159, rho = -0.825533 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -3.459209, rho = -0.749038 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -4.781417, rho = -0.639004 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -6.475421, rho = -0.480725 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 77% (77/100) (classification) Accuracy = 66% (660/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -8.481930, rho = -0.253049 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 94% (94/100) (classification) Accuracy = 94.7% (947/1000) (classification) * optimization finished, #iter = 43 nu = 0.798703 obj = -10.734355, rho = -0.209008 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 97% (97/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 57 nu = 0.715105 obj = -13.333271, rho = -0.111455 nSV = 76, nBSV = 70 Total nSV = 76 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 42 nu = 0.632884 obj = -16.294837, rho = -0.142310 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 93 nu = 0.530394 obj = -19.658304, rho = -0.136987 nSV = 57, nBSV = 49 Total nSV = 57 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 41 nu = 0.455298 obj = -23.792532, rho = -0.087382 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 69 nu = 0.379947 obj = -28.515910, rho = -0.162173 nSV = 42, nBSV = 33 Total nSV = 42 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 71 nu = 0.310611 obj = -34.397698, rho = -0.151315 nSV = 36, nBSV = 27 Total nSV = 36 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.256473 obj = -41.977377, rho = -0.177193 nSV = 33, nBSV = 23 Total nSV = 33 Accuracy = 97% (97/100) (classification) Accuracy = 96.8% (968/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.221088 obj = -51.590205, rho = -0.084543 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 96.8% (968/1000) (classification) *.*.* optimization finished, #iter = 166 nu = 0.191273 obj = -63.264682, rho = 0.118210 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 96% (96/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 93 nu = 0.158706 obj = -77.221537, rho = 0.028169 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 96% (96/100) (classification) Accuracy = 96.6% (966/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.133196 obj = -96.032756, rho = 0.049580 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 96% (96/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -0.915914, rho = -0.922143 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.302305, rho = -0.888006 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.841873, rho = -0.839727 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.584406, rho = -0.769456 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.582973, rho = -0.668374 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -4.875492, rho = -0.522973 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 76% (76/100) (classification) Accuracy = 74.9% (749/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -6.437028, rho = -0.313820 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 96% (96/100) (classification) Accuracy = 95.1% (951/1000) (classification) * optimization finished, #iter = 49 nu = 0.883359 obj = -8.193442, rho = -0.214003 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 47 nu = 0.788164 obj = -10.186828, rho = -0.234327 nSV = 81, nBSV = 77 Total nSV = 81 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 38 nu = 0.683333 obj = -12.565519, rho = -0.299208 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 61 nu = 0.593258 obj = -15.290637, rho = -0.253605 nSV = 63, nBSV = 56 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 52 nu = 0.501496 obj = -18.536278, rho = -0.227616 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 40 nu = 0.422110 obj = -22.373277, rho = -0.212565 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 87 nu = 0.359247 obj = -26.775895, rho = -0.158849 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 99.3% (993/1000) (classification) * optimization finished, #iter = 43 nu = 0.304605 obj = -31.879759, rho = -0.222517 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 99 nu = 0.252247 obj = -37.357160, rho = -0.364722 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 66 nu = 0.204149 obj = -43.289142, rho = -0.402986 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 84 nu = 0.162874 obj = -50.589709, rho = -0.374323 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 46 nu = 0.137661 obj = -59.485411, rho = -0.551977 nSV = 16, nBSV = 11 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.111300 obj = -67.532640, rho = -0.616631 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -0.876508, rho = -0.943183 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -1.245997, rho = -0.918272 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -1.761647, rho = -0.882439 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -2.470608, rho = -0.830894 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -3.422594, rho = -0.756749 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -4.651652, rho = -0.650096 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 61% (61/100) (classification) Accuracy = 55.5% (555/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -6.129236, rho = -0.496681 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 88% (88/100) (classification) Accuracy = 85% (850/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -7.705807, rho = -0.337518 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 97% (97/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 43 nu = 0.749063 obj = -9.374003, rho = -0.311796 nSV = 78, nBSV = 73 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 35 nu = 0.632458 obj = -11.330600, rho = -0.229668 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 79 nu = 0.533107 obj = -13.677419, rho = -0.206931 nSV = 57, nBSV = 49 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 31 nu = 0.451984 obj = -16.528605, rho = -0.180786 nSV = 47, nBSV = 44 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 54 nu = 0.382887 obj = -19.711034, rho = -0.163429 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 47 nu = 0.316161 obj = -23.298076, rho = -0.086151 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 68 nu = 0.265011 obj = -27.375399, rho = -0.084331 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 61 nu = 0.215714 obj = -32.150694, rho = -0.086008 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 49 nu = 0.175811 obj = -37.850152, rho = -0.136033 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 44 nu = 0.148101 obj = -44.026958, rho = -0.109799 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 99 nu = 0.116427 obj = -50.540987, rho = -0.083327 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 68 nu = 0.097932 obj = -57.782555, rho = 0.036155 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 45 nu = 0.840000 obj = -0.821630, rho = 0.929079 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 45 nu = 0.840000 obj = -1.170288, rho = 0.897983 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 45 nu = 0.840000 obj = -1.659428, rho = 0.853254 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 44 nu = 0.840000 obj = -2.337401, rho = 0.788913 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 44 nu = 0.840000 obj = -3.259599, rho = 0.696362 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 45 nu = 0.840000 obj = -4.476405, rho = 0.563231 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -5.999671, rho = 0.371730 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 79% (79/100) (classification) Accuracy = 71.2% (712/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -7.721020, rho = 0.096265 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 97% (97/100) (classification) Accuracy = 94.2% (942/1000) (classification) * optimization finished, #iter = 45 nu = 0.751077 obj = -9.552774, rho = 0.053380 nSV = 76, nBSV = 72 Total nSV = 76 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 39 nu = 0.648240 obj = -11.691155, rho = 0.089703 nSV = 67, nBSV = 64 Total nSV = 67 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 42 nu = 0.556454 obj = -14.135978, rho = 0.069971 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 38 nu = 0.468922 obj = -16.961413, rho = 0.074873 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 32 nu = 0.396818 obj = -20.259059, rho = 0.055919 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 49 nu = 0.334260 obj = -23.718107, rho = 0.109417 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 66 nu = 0.269110 obj = -27.687519, rho = 0.153930 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 87 nu = 0.219687 obj = -32.227419, rho = 0.230296 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 138 nu = 0.184183 obj = -36.849536, rho = 0.195450 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 250 nu = 0.140586 obj = -41.583807, rho = 0.195882 nSV = 20, nBSV = 9 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 243 nu = 0.111635 obj = -47.860886, rho = 0.171585 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) ....* optimization finished, #iter = 426 nu = 0.086822 obj = -55.267969, rho = 0.190243 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -0.932656, rho = 0.883544 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.324334, rho = 0.832484 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.869305, rho = 0.759036 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -2.615067, rho = 0.653386 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.608872, rho = 0.501412 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.875076, rho = 0.282807 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 74% (74/100) (classification) Accuracy = 70.8% (708/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -6.358487, rho = -0.005599 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 99% (99/100) (classification) Accuracy = 93.8% (938/1000) (classification) * optimization finished, #iter = 59 nu = 0.868481 obj = -8.030845, rho = -0.070340 nSV = 90, nBSV = 84 Total nSV = 90 Accuracy = 99% (99/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 53 nu = 0.768915 obj = -10.006352, rho = -0.097133 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 38 nu = 0.662833 obj = -12.431517, rho = -0.122732 nSV = 68, nBSV = 66 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 39 nu = 0.583215 obj = -15.223303, rho = -0.142831 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 38 nu = 0.500000 obj = -18.426315, rho = -0.106049 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 40 nu = 0.412222 obj = -22.282791, rho = -0.100404 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 53 nu = 0.347239 obj = -27.296343, rho = -0.083245 nSV = 37, nBSV = 33 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 28 nu = 0.299731 obj = -33.184023, rho = -0.122209 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 44 nu = 0.260974 obj = -39.976906, rho = -0.051578 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 55 nu = 0.211972 obj = -47.758566, rho = -0.100101 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.178698 obj = -56.912376, rho = -0.036914 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 84 nu = 0.146906 obj = -67.967195, rho = -0.067770 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 68 nu = 0.119692 obj = -81.808794, rho = -0.099642 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -0.932946, rho = -0.900162 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.324933, rho = -0.856388 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.870545, rho = -0.793421 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.617633, rho = -0.702846 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.614181, rho = -0.572559 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 56% (56/100) (classification) Accuracy = 53.1% (531/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.886061, rho = -0.385147 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 85% (85/100) (classification) Accuracy = 83.6% (836/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.398347, rho = -0.242440 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 95% (95/100) (classification) Accuracy = 94.2% (942/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -8.170748, rho = -0.219514 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 97% (97/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 42 nu = 0.780000 obj = -10.285784, rho = -0.157817 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 51 nu = 0.686989 obj = -12.713227, rho = -0.063397 nSV = 72, nBSV = 66 Total nSV = 72 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 50 nu = 0.594487 obj = -15.600800, rho = -0.090195 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 45 nu = 0.513266 obj = -19.024245, rho = -0.192202 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 66 nu = 0.432549 obj = -22.996396, rho = -0.239050 nSV = 47, nBSV = 40 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.354937 obj = -27.916692, rho = -0.241246 nSV = 41, nBSV = 33 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 70 nu = 0.296652 obj = -34.490412, rho = -0.219542 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 49 nu = 0.263222 obj = -42.849235, rho = -0.265204 nSV = 29, nBSV = 25 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 72 nu = 0.228160 obj = -51.815795, rho = -0.280417 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.193502 obj = -62.293804, rho = -0.279022 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 196 nu = 0.158283 obj = -75.314857, rho = -0.281864 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*..* optimization finished, #iter = 377 nu = 0.133143 obj = -91.201439, rho = -0.374078 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -0.825996, rho = -0.954935 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -1.179321, rho = -0.935176 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -1.678119, rho = -0.906754 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -2.376077, rho = -0.865870 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -3.339625, rho = -0.807061 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -4.641989, rho = -0.722466 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -6.342288, rho = -0.600782 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 61% (61/100) (classification) Accuracy = 55.3% (553/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -8.429942, rho = -0.425745 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 87% (87/100) (classification) Accuracy = 88.8% (888/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -10.719869, rho = -0.247509 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 39 nu = 0.720000 obj = -13.217794, rho = -0.173018 nSV = 74, nBSV = 70 Total nSV = 74 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 35 nu = 0.615282 obj = -16.229028, rho = -0.117304 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 43 nu = 0.531147 obj = -19.777223, rho = -0.051323 nSV = 56, nBSV = 49 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 47 nu = 0.445649 obj = -24.080829, rho = -0.048088 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 33 nu = 0.379110 obj = -29.444514, rho = -0.070853 nSV = 40, nBSV = 36 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 39 nu = 0.326799 obj = -35.711631, rho = -0.077522 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 161 nu = 0.274607 obj = -42.721378, rho = -0.113178 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 54 nu = 0.228729 obj = -51.448325, rho = -0.120824 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 67 nu = 0.193008 obj = -61.716755, rho = -0.206413 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 69 nu = 0.165225 obj = -73.037317, rho = -0.135509 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 79 nu = 0.141169 obj = -84.285557, rho = -0.170292 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 47 nu = 0.860000 obj = -0.840800, rho = 0.928512 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 47 nu = 0.860000 obj = -1.197339, rho = 0.897168 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -1.697257, rho = 0.852081 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 49 nu = 0.860000 obj = -2.389575, rho = 0.787538 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 49 nu = 0.860000 obj = -3.330011, rho = 0.694384 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 47 nu = 0.860000 obj = -4.568093, rho = 0.560387 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 60% (60/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -6.111712, rho = 0.367077 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 80% (80/100) (classification) Accuracy = 70.9% (709/1000) (classification) * optimization finished, #iter = 47 nu = 0.825451 obj = -7.863844, rho = 0.163221 nSV = 84, nBSV = 82 Total nSV = 84 Accuracy = 91% (91/100) (classification) Accuracy = 91.5% (915/1000) (classification) * optimization finished, #iter = 46 nu = 0.756514 obj = -9.910321, rho = -0.001135 nSV = 77, nBSV = 74 Total nSV = 77 Accuracy = 96% (96/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 43 nu = 0.676482 obj = -12.174851, rho = -0.036288 nSV = 69, nBSV = 64 Total nSV = 69 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 38 nu = 0.568258 obj = -14.850068, rho = -0.020863 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.478583 obj = -18.094400, rho = -0.047565 nSV = 52, nBSV = 45 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 79 nu = 0.412349 obj = -22.078747, rho = 0.013842 nSV = 44, nBSV = 36 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 31 nu = 0.343842 obj = -26.908692, rho = 0.087660 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 54 nu = 0.289408 obj = -32.856379, rho = 0.103475 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 77 nu = 0.243328 obj = -40.540976, rho = 0.113686 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.205884 obj = -50.474298, rho = 0.187667 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 39 nu = 0.183965 obj = -63.321484, rho = 0.175403 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 87 nu = 0.162240 obj = -78.120557, rho = 0.138790 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 82 nu = 0.139150 obj = -95.469234, rho = 0.064244 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.963958, rho = -0.040596 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 94.8% (948/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.363874, rho = -0.058395 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 94.8% (948/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.914831, rho = -0.083999 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 94.8% (948/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.657068, rho = -0.120828 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 94.8% (948/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.620690, rho = -0.173805 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 94.8% (948/1000) (classification) * optimization finished, #iter = 50 nu = 0.991142 obj = -4.792211, rho = -0.243044 nSV = 100, nBSV = 98 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 47 nu = 0.926253 obj = -6.155596, rho = -0.287493 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 94% (94/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 44 nu = 0.837646 obj = -7.805853, rho = -0.218088 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 96% (96/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 51 nu = 0.734791 obj = -9.754554, rho = -0.165826 nSV = 76, nBSV = 71 Total nSV = 76 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 39 nu = 0.644020 obj = -12.191325, rho = -0.265547 nSV = 67, nBSV = 63 Total nSV = 67 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 42 nu = 0.558544 obj = -15.167087, rho = -0.240181 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 41 nu = 0.486158 obj = -18.856232, rho = -0.169287 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 28 nu = 0.423971 obj = -23.276440, rho = -0.215243 nSV = 44, nBSV = 41 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 33 nu = 0.362877 obj = -28.467507, rho = -0.256051 nSV = 39, nBSV = 35 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 33 nu = 0.309487 obj = -34.835773, rho = -0.212928 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 49 nu = 0.270912 obj = -42.481525, rho = -0.182782 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 214 nu = 0.228154 obj = -50.901904, rho = -0.270875 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 28 nu = 0.188669 obj = -61.598935, rho = -0.359379 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.160202 obj = -73.707640, rho = -0.324859 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 215 nu = 0.131270 obj = -88.322047, rho = -0.378287 nSV = 19, nBSV = 8 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.947594, rho = -0.883936 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.342628, rho = -0.833047 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.889013, rho = -0.759847 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.629748, rho = -0.654552 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.601705, rho = -0.503091 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 59% (59/100) (classification) Accuracy = 57.6% (576/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.806244, rho = -0.285221 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 90% (90/100) (classification) Accuracy = 89.3% (893/1000) (classification) * optimization finished, #iter = 51 nu = 0.938803 obj = -6.173575, rho = -0.244564 nSV = 96, nBSV = 92 Total nSV = 96 Accuracy = 96% (96/100) (classification) Accuracy = 94.4% (944/1000) (classification) * optimization finished, #iter = 49 nu = 0.853248 obj = -7.746784, rho = -0.167319 nSV = 88, nBSV = 84 Total nSV = 88 Accuracy = 96% (96/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 47 nu = 0.755039 obj = -9.529320, rho = -0.113665 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 34 nu = 0.643767 obj = -11.633298, rho = -0.076907 nSV = 66, nBSV = 64 Total nSV = 66 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 48 nu = 0.558665 obj = -13.954920, rho = -0.047471 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 47 nu = 0.468594 obj = -16.504575, rho = 0.006219 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 49 nu = 0.383315 obj = -19.471225, rho = -0.009416 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 57 nu = 0.316460 obj = -23.063056, rho = 0.066985 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 57 nu = 0.262031 obj = -27.053444, rho = 0.125798 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.212137 obj = -31.519943, rho = 0.110416 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.171233 obj = -37.019061, rho = 0.123222 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 63 nu = 0.139608 obj = -43.916281, rho = 0.090119 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.123807 obj = -50.108495, rho = -0.012275 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 129 nu = 0.097837 obj = -54.517223, rho = 0.001171 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.780000 obj = -0.764853, rho = 0.938555 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 61% (61/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 44 nu = 0.780000 obj = -1.090650, rho = 0.911744 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 61% (61/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 43 nu = 0.780000 obj = -1.549078, rho = 0.873048 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 61% (61/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 47 nu = 0.780000 obj = -2.187377, rho = 0.817641 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 61% (61/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 49 nu = 0.780000 obj = -3.061812, rho = 0.737818 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 61% (61/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 43 nu = 0.780000 obj = -4.229176, rho = 0.623543 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 61% (61/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 57 nu = 0.780000 obj = -5.721163, rho = 0.458167 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 70% (70/100) (classification) Accuracy = 58.9% (589/1000) (classification) * optimization finished, #iter = 57 nu = 0.780000 obj = -7.479974, rho = 0.220734 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 91% (91/100) (classification) Accuracy = 86.5% (865/1000) (classification) * optimization finished, #iter = 42 nu = 0.736966 obj = -9.357791, rho = 0.004356 nSV = 76, nBSV = 71 Total nSV = 76 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 44 nu = 0.644594 obj = -11.387645, rho = 0.062304 nSV = 66, nBSV = 63 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 61 nu = 0.541642 obj = -13.656281, rho = 0.081064 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 52 nu = 0.459366 obj = -16.287165, rho = 0.043660 nSV = 49, nBSV = 41 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 49 nu = 0.375830 obj = -19.337061, rho = 0.019077 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 36 nu = 0.316821 obj = -22.871641, rho = -0.022757 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.260845 obj = -26.668349, rho = -0.038600 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 52 nu = 0.212291 obj = -30.881967, rho = -0.061487 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 53 nu = 0.168457 obj = -36.002047, rho = -0.141774 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 90 nu = 0.136781 obj = -41.912039, rho = -0.150868 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 152 nu = 0.110660 obj = -48.995571, rho = -0.168959 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*..* optimization finished, #iter = 323 nu = 0.091018 obj = -56.944687, rho = -0.242054 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -0.861537, rho = -0.937984 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -1.227634, rho = -0.910793 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -1.741797, rho = -0.871680 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -2.455634, rho = -0.815417 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -3.429155, rho = -0.734487 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -4.719231, rho = -0.618073 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 58% (58/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -6.346749, rho = -0.450617 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 80% (80/100) (classification) Accuracy = 77.2% (772/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -8.227772, rho = -0.256046 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 95% (95/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 45 nu = 0.786530 obj = -10.356719, rho = -0.206479 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 96% (96/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 37 nu = 0.688492 obj = -12.856316, rho = -0.155841 nSV = 70, nBSV = 67 Total nSV = 70 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 57 nu = 0.585525 obj = -15.940140, rho = -0.129178 nSV = 62, nBSV = 55 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 29 nu = 0.500492 obj = -20.040272, rho = -0.084528 nSV = 52, nBSV = 50 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 38 nu = 0.448883 obj = -25.081528, rho = -0.208687 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.386141 obj = -31.303059, rho = -0.200857 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 35 nu = 0.341780 obj = -38.647618, rho = -0.074962 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 73 nu = 0.295519 obj = -47.002741, rho = -0.143306 nSV = 34, nBSV = 25 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 52 nu = 0.245602 obj = -57.462113, rho = -0.197793 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 62 nu = 0.215434 obj = -70.590926, rho = -0.208052 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 181 nu = 0.185285 obj = -84.618654, rho = -0.212166 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 63 nu = 0.151742 obj = -101.547345, rho = -0.286608 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.952762, rho = -0.903911 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.353322, rho = -0.861781 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.911141, rho = -0.801178 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.675534, rho = -0.714005 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.696442, rho = -0.588610 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 52% (52/100) (classification) Accuracy = 52.3% (523/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -5.002266, rho = -0.408237 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 81% (81/100) (classification) Accuracy = 76.9% (769/1000) (classification) * optimization finished, #iter = 49 nu = 0.962611 obj = -6.547559, rho = -0.227208 nSV = 98, nBSV = 96 Total nSV = 98 Accuracy = 96% (96/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 48 nu = 0.901590 obj = -8.327254, rho = -0.185704 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 47 nu = 0.801732 obj = -10.295377, rho = -0.145320 nSV = 82, nBSV = 78 Total nSV = 82 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 43 nu = 0.680098 obj = -12.682010, rho = -0.132910 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 37 nu = 0.596917 obj = -15.608633, rho = -0.187298 nSV = 61, nBSV = 58 Total nSV = 61 Accuracy = 97% (97/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 36 nu = 0.506921 obj = -18.998129, rho = -0.229270 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 33 nu = 0.436978 obj = -23.037010, rho = -0.177596 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 37 nu = 0.367261 obj = -27.720290, rho = -0.158730 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 97% (97/100) (classification) Accuracy = 99.1% (991/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.307603 obj = -33.190265, rho = -0.164453 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 97% (97/100) (classification) Accuracy = 99.1% (991/1000) (classification) *.* optimization finished, #iter = 175 nu = 0.255629 obj = -39.640758, rho = -0.206579 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 146 nu = 0.208413 obj = -47.714856, rho = -0.240966 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*..* optimization finished, #iter = 311 nu = 0.172047 obj = -57.899710, rho = -0.279123 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) ...*.* optimization finished, #iter = 421 nu = 0.144893 obj = -71.587164, rho = -0.313647 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) ..*..* optimization finished, #iter = 452 nu = 0.125016 obj = -89.226343, rho = -0.459926 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -0.914085, rho = -0.935800 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.298521, rho = -0.907652 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.834038, rho = -0.867162 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.568197, rho = -0.808919 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.549432, rho = -0.725140 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -4.806091, rho = -0.604628 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 76% (76/100) (classification) Accuracy = 72.2% (722/1000) (classification) * optimization finished, #iter = 49 nu = 0.933130 obj = -6.294051, rho = -0.446552 nSV = 95, nBSV = 92 Total nSV = 95 Accuracy = 91% (91/100) (classification) Accuracy = 92.2% (922/1000) (classification) * optimization finished, #iter = 45 nu = 0.852895 obj = -8.014083, rho = -0.427064 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 93% (93/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 39 nu = 0.759852 obj = -10.107360, rho = -0.400417 nSV = 76, nBSV = 74 Total nSV = 76 Accuracy = 95% (95/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 48 nu = 0.667099 obj = -12.636401, rho = -0.352269 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 40 nu = 0.590552 obj = -15.652594, rho = -0.299093 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 52 nu = 0.513752 obj = -19.149009, rho = -0.342639 nSV = 55, nBSV = 48 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.434871 obj = -23.240073, rho = -0.347948 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 44 nu = 0.371186 obj = -28.063824, rho = -0.326882 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 58 nu = 0.319286 obj = -33.330115, rho = -0.339502 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.256582 obj = -39.321327, rho = -0.338237 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 63 nu = 0.219921 obj = -46.458255, rho = -0.402271 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 143 nu = 0.173832 obj = -54.386092, rho = -0.409111 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 193 nu = 0.141045 obj = -63.966471, rho = -0.406605 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..*..* optimization finished, #iter = 401 nu = 0.117032 obj = -75.939119, rho = -0.429275 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.949978, rho = 0.838755 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.347561, rho = 0.768057 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.899225, rho = 0.665949 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -2.650877, rho = 0.519484 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -3.645424, rho = 0.308802 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 60% (60/100) (classification) Accuracy = 56% (560/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -4.896703, rho = 0.005746 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 84% (84/100) (classification) Accuracy = 80.7% (807/1000) (classification) * optimization finished, #iter = 51 nu = 0.951946 obj = -6.339382, rho = -0.267752 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 94% (94/100) (classification) Accuracy = 93.7% (937/1000) (classification) * optimization finished, #iter = 72 nu = 0.857681 obj = -8.016717, rho = -0.258019 nSV = 91, nBSV = 83 Total nSV = 91 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 49 nu = 0.778383 obj = -10.017508, rho = -0.191544 nSV = 80, nBSV = 76 Total nSV = 80 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.681436 obj = -12.225272, rho = -0.172334 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 37 nu = 0.580000 obj = -14.754318, rho = -0.196995 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 51 nu = 0.481497 obj = -17.784973, rho = -0.180917 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 31 nu = 0.405882 obj = -21.469556, rho = -0.149793 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 43 nu = 0.337293 obj = -25.873788, rho = -0.180687 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 37 nu = 0.281659 obj = -31.329465, rho = -0.169772 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 48 nu = 0.239828 obj = -38.061095, rho = -0.238454 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 59 nu = 0.198563 obj = -46.072563, rho = -0.266949 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 61 nu = 0.167637 obj = -56.389732, rho = -0.261462 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 53 nu = 0.142625 obj = -69.430889, rho = -0.351751 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.123958 obj = -85.439964, rho = -0.445461 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.949420, rho = 0.833878 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.346406, rho = 0.761041 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.896831, rho = 0.656270 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.645923, rho = 0.505561 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.635173, rho = 0.288775 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 61% (61/100) (classification) Accuracy = 62% (620/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.875494, rho = -0.023062 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 86% (86/100) (classification) Accuracy = 86.5% (865/1000) (classification) * optimization finished, #iter = 51 nu = 0.935511 obj = -6.313500, rho = -0.187288 nSV = 96, nBSV = 92 Total nSV = 96 Accuracy = 96% (96/100) (classification) Accuracy = 94.5% (945/1000) (classification) * optimization finished, #iter = 47 nu = 0.855472 obj = -8.006987, rho = -0.205753 nSV = 88, nBSV = 84 Total nSV = 88 Accuracy = 97% (97/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 40 nu = 0.758661 obj = -10.067099, rho = -0.278581 nSV = 76, nBSV = 74 Total nSV = 76 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 47 nu = 0.667637 obj = -12.540135, rho = -0.208200 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.569728 obj = -15.563814, rho = -0.199165 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 55 nu = 0.495280 obj = -19.315122, rho = -0.271366 nSV = 53, nBSV = 46 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 30 nu = 0.428367 obj = -24.115598, rho = -0.296478 nSV = 44, nBSV = 41 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 46 nu = 0.370422 obj = -30.032032, rho = -0.367956 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 30 nu = 0.323106 obj = -37.540148, rho = -0.356257 nSV = 34, nBSV = 30 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 46 nu = 0.283625 obj = -46.626750, rho = -0.553661 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 57 nu = 0.241684 obj = -57.501098, rho = -0.565040 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 75 nu = 0.208427 obj = -71.216721, rho = -0.611153 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 61 nu = 0.185195 obj = -87.332558, rho = -0.653674 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 136 nu = 0.156416 obj = -105.861995, rho = -0.640303 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -0.824781, rho = -0.960455 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -1.176808, rho = -0.943117 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -1.672920, rho = -0.918176 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -2.365318, rho = -0.882301 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -3.317363, rho = -0.830696 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -4.595926, rho = -0.756464 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -6.246976, rho = -0.649686 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 68% (68/100) (classification) Accuracy = 57.9% (579/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -8.232728, rho = -0.496091 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 87% (87/100) (classification) Accuracy = 85.3% (853/1000) (classification) * optimization finished, #iter = 56 nu = 0.780000 obj = -10.457424, rho = -0.381374 nSV = 80, nBSV = 76 Total nSV = 80 Accuracy = 96% (96/100) (classification) Accuracy = 93.4% (934/1000) (classification) * optimization finished, #iter = 47 nu = 0.695100 obj = -12.951400, rho = -0.320657 nSV = 72, nBSV = 67 Total nSV = 72 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 47 nu = 0.604125 obj = -16.035881, rho = -0.240774 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 81 nu = 0.521974 obj = -19.644929, rho = -0.133097 nSV = 56, nBSV = 48 Total nSV = 56 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 51 nu = 0.436532 obj = -24.163992, rho = -0.114830 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 35 nu = 0.389706 obj = -29.693647, rho = -0.090070 nSV = 40, nBSV = 36 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 63 nu = 0.325509 obj = -35.921466, rho = -0.096161 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 60 nu = 0.275227 obj = -43.279575, rho = -0.157589 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 175 nu = 0.239219 obj = -51.831339, rho = -0.116696 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 97 nu = 0.195368 obj = -61.402261, rho = -0.139811 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 52 nu = 0.163536 obj = -72.114698, rho = -0.150782 nSV = 19, nBSV = 14 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.137020 obj = -81.646684, rho = -0.186674 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -0.897591, rho = 0.894151 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 54.9% (549/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -1.277007, rho = 0.847741 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 54.9% (549/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -1.807668, rho = 0.780983 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 54.9% (549/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -2.539732, rho = 0.684955 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 54.9% (549/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -3.528079, rho = 0.546824 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 54.9% (549/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -4.815912, rho = 0.348129 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 66% (66/100) (classification) Accuracy = 65% (650/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -6.391431, rho = 0.062316 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 86% (86/100) (classification) Accuracy = 88.3% (883/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -8.193664, rho = -0.149786 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 95% (95/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 44 nu = 0.777099 obj = -10.243124, rho = -0.138065 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 45 nu = 0.685599 obj = -12.743201, rho = -0.099374 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 55 nu = 0.585518 obj = -15.741101, rho = -0.118512 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 48 nu = 0.518189 obj = -19.337412, rho = -0.073751 nSV = 53, nBSV = 49 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 74 nu = 0.436473 obj = -23.511472, rho = -0.081657 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 52 nu = 0.365481 obj = -28.771516, rho = -0.099806 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.314823 obj = -35.257216, rho = -0.106023 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 55 nu = 0.269663 obj = -43.050824, rho = -0.161022 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 63 nu = 0.228351 obj = -52.165469, rho = -0.205494 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.193488 obj = -63.082074, rho = -0.202248 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 59 nu = 0.161627 obj = -76.544543, rho = -0.174844 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.138640 obj = -91.438130, rho = -0.248106 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -0.878600, rho = -0.926221 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -1.250324, rho = -0.893873 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -1.770602, rho = -0.847341 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -2.489137, rho = -0.780408 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -3.460933, rho = -0.684127 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -4.730981, rho = -0.545633 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 64% (64/100) (classification) Accuracy = 55.4% (554/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -6.293379, rho = -0.346416 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 91% (91/100) (classification) Accuracy = 89.2% (892/1000) (classification) * optimization finished, #iter = 49 nu = 0.864847 obj = -8.040077, rho = -0.142932 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 42 nu = 0.769223 obj = -10.062265, rho = -0.073821 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 43 nu = 0.690134 obj = -12.336515, rho = -0.123260 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 35 nu = 0.592786 obj = -14.849392, rho = -0.098046 nSV = 60, nBSV = 58 Total nSV = 60 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 41 nu = 0.500000 obj = -17.572058, rho = -0.080309 nSV = 52, nBSV = 49 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 60 nu = 0.412297 obj = -20.499104, rho = -0.027302 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 54 nu = 0.338770 obj = -23.766808, rho = -0.049870 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 92 nu = 0.271221 obj = -27.314548, rho = -0.014350 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 145 nu = 0.214008 obj = -31.504592, rho = 0.000926 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 68 nu = 0.174837 obj = -36.567031, rho = 0.082775 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 85 nu = 0.144444 obj = -41.774285, rho = 0.141076 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 85 nu = 0.115686 obj = -46.594574, rho = 0.040747 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 118 nu = 0.087481 obj = -51.698744, rho = 0.042116 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -0.916004, rho = -0.929098 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -1.302492, rho = -0.898010 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -1.842255, rho = -0.853293 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -2.585198, rho = -0.788969 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -3.584611, rho = -0.696443 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -4.878881, rho = -0.563348 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 59% (59/100) (classification) Accuracy = 56.1% (561/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -6.444042, rho = -0.371898 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 89% (89/100) (classification) Accuracy = 89.1% (891/1000) (classification) * optimization finished, #iter = 48 nu = 0.897203 obj = -8.194425, rho = -0.179893 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 48 nu = 0.797730 obj = -10.165818, rho = -0.120707 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 47 nu = 0.688308 obj = -12.421418, rho = -0.045778 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 71 nu = 0.591766 obj = -15.045548, rho = -0.014693 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 62 nu = 0.498360 obj = -18.027967, rho = 0.039630 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 33 nu = 0.409726 obj = -21.672469, rho = -0.016525 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.339510 obj = -26.066367, rho = 0.022236 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 43 nu = 0.295976 obj = -31.531373, rho = -0.119262 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 96 nu = 0.253328 obj = -37.178633, rho = -0.125058 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.201506 obj = -43.375069, rho = -0.128686 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 162 nu = 0.165577 obj = -51.170764, rho = -0.184590 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 35 nu = 0.140311 obj = -58.996116, rho = -0.242816 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.112810 obj = -65.760045, rho = -0.229437 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -0.933124, rho = 0.858047 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.325302, rho = 0.795807 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.871308, rho = 0.706279 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -2.619213, rho = 0.577497 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.617449, rho = 0.392250 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.892823, rho = 0.125783 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 83% (83/100) (classification) Accuracy = 75.7% (757/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -6.395207, rho = -0.243541 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 94% (94/100) (classification) Accuracy = 92.4% (924/1000) (classification) * optimization finished, #iter = 47 nu = 0.871087 obj = -8.089050, rho = -0.208014 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 95% (95/100) (classification) Accuracy = 94.6% (946/1000) (classification) * optimization finished, #iter = 42 nu = 0.778944 obj = -10.075685, rho = -0.220465 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 98% (98/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 37 nu = 0.671089 obj = -12.396379, rho = -0.160456 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 50 nu = 0.573794 obj = -15.256193, rho = -0.095688 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 38 nu = 0.500000 obj = -18.733468, rho = -0.144127 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 55 nu = 0.422877 obj = -22.837308, rho = -0.134340 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 59 nu = 0.364072 obj = -27.670159, rho = -0.074092 nSV = 38, nBSV = 33 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 43 nu = 0.307489 obj = -33.235861, rho = -0.039862 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 52 nu = 0.259875 obj = -39.596217, rho = -0.030473 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.215772 obj = -46.818080, rho = 0.027589 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 49 nu = 0.175243 obj = -55.483904, rho = -0.023486 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.144612 obj = -66.216708, rho = -0.070580 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 96 nu = 0.121963 obj = -78.385640, rho = -0.061605 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -0.931922, rho = -0.918150 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.322815, rho = -0.882263 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.866163, rho = -0.830641 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -2.608567, rho = -0.756385 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -3.595421, rho = -0.649572 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 57% (57/100) (classification) Accuracy = 52.4% (524/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -4.847244, rho = -0.495927 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 79% (79/100) (classification) Accuracy = 78.9% (789/1000) (classification) * optimization finished, #iter = 50 nu = 0.929288 obj = -6.316209, rho = -0.345958 nSV = 95, nBSV = 92 Total nSV = 95 Accuracy = 95% (95/100) (classification) Accuracy = 94.4% (944/1000) (classification) * optimization finished, #iter = 45 nu = 0.859852 obj = -8.059301, rho = -0.292912 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 42 nu = 0.772708 obj = -10.114651, rho = -0.209690 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 43 nu = 0.672528 obj = -12.559212, rho = -0.147958 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 32 nu = 0.598070 obj = -15.490376, rho = -0.242388 nSV = 60, nBSV = 58 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 58 nu = 0.508534 obj = -18.795638, rho = -0.193423 nSV = 54, nBSV = 46 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 38 nu = 0.424086 obj = -22.842220, rho = -0.230559 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 61 nu = 0.361470 obj = -27.758914, rho = -0.180452 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 50 nu = 0.306774 obj = -33.537910, rho = -0.157415 nSV = 34, nBSV = 30 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 35 nu = 0.260000 obj = -40.639941, rho = -0.231216 nSV = 27, nBSV = 24 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 74 nu = 0.213534 obj = -48.937422, rho = -0.242657 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 53 nu = 0.179180 obj = -59.531675, rho = -0.207326 nSV = 20, nBSV = 15 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 81 nu = 0.153970 obj = -72.424072, rho = -0.169281 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 85 nu = 0.133106 obj = -87.066374, rho = -0.223343 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -0.803153, rho = 0.931118 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 47 nu = 0.820000 obj = -1.144676, rho = 0.899402 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 48 nu = 0.820000 obj = -1.624579, rho = 0.856153 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 48 nu = 0.820000 obj = -2.291394, rho = 0.793083 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 48 nu = 0.820000 obj = -3.201947, rho = 0.702361 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 46 nu = 0.820000 obj = -4.411118, rho = 0.571994 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 46 nu = 0.820000 obj = -5.942265, rho = 0.384335 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 72% (72/100) (classification) Accuracy = 67.2% (672/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -7.713979, rho = 0.114397 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 95% (95/100) (classification) Accuracy = 92.3% (923/1000) (classification) * optimization finished, #iter = 48 nu = 0.749245 obj = -9.644828, rho = -0.025939 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 57 nu = 0.664768 obj = -11.759693, rho = -0.078713 nSV = 69, nBSV = 64 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 68 nu = 0.565899 obj = -14.138074, rho = -0.114614 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 41 nu = 0.463365 obj = -16.930080, rho = -0.074999 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 30 nu = 0.398383 obj = -20.262935, rho = -0.102009 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 87 nu = 0.329966 obj = -23.620781, rho = -0.215085 nSV = 37, nBSV = 28 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 27 nu = 0.266986 obj = -27.782529, rho = -0.186735 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 68 nu = 0.221625 obj = -32.003019, rho = -0.290336 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 53 nu = 0.178208 obj = -36.471555, rho = -0.346579 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 69 nu = 0.142135 obj = -41.405800, rho = -0.504203 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *..* optimization finished, #iter = 219 nu = 0.117012 obj = -46.254762, rho = -0.433599 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..* optimization finished, #iter = 297 nu = 0.088446 obj = -50.569373, rho = -0.426082 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -0.916717, rho = -0.938845 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.303968, rho = -0.912032 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.845310, rho = -0.873462 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -2.591518, rho = -0.817981 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -3.597688, rho = -0.738175 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.905939, rho = -0.623378 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 57% (57/100) (classification) Accuracy = 53.5% (535/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.500027, rho = -0.458248 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 85% (85/100) (classification) Accuracy = 84.7% (847/1000) (classification) * optimization finished, #iter = 47 nu = 0.913458 obj = -8.227631, rho = -0.277524 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 42 nu = 0.786818 obj = -10.179549, rho = -0.268859 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 44 nu = 0.693918 obj = -12.430848, rho = -0.207962 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 53 nu = 0.585851 obj = -14.934264, rho = -0.208745 nSV = 63, nBSV = 56 Total nSV = 63 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 44 nu = 0.493813 obj = -17.950074, rho = -0.188819 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 47 nu = 0.411937 obj = -21.468802, rho = -0.172639 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 38 nu = 0.350374 obj = -25.357691, rho = -0.065839 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 66 nu = 0.282480 obj = -29.905592, rho = -0.108533 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 67 nu = 0.241048 obj = -35.260614, rho = -0.233229 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 80 nu = 0.190376 obj = -41.050987, rho = -0.217199 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.157638 obj = -48.143373, rho = -0.276897 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.125668 obj = -56.301602, rho = -0.245759 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 196 nu = 0.106226 obj = -65.103092, rho = -0.164522 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -0.841589, rho = 0.916463 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -1.198972, rho = 0.879836 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -1.700635, rho = 0.827150 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -2.396564, rho = 0.751364 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -3.344474, rho = 0.642350 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -4.598018, rho = 0.485539 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 58% (58/100) (classification) Accuracy = 52.7% (527/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -6.173623, rho = 0.259973 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 81% (81/100) (classification) Accuracy = 73.3% (733/1000) (classification) * optimization finished, #iter = 45 nu = 0.840000 obj = -7.997549, rho = 0.016987 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 92% (92/100) (classification) Accuracy = 90.8% (908/1000) (classification) * optimization finished, #iter = 48 nu = 0.750110 obj = -10.106457, rho = 0.021460 nSV = 77, nBSV = 72 Total nSV = 77 Accuracy = 95% (95/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 40 nu = 0.680000 obj = -12.615931, rho = -0.018693 nSV = 69, nBSV = 66 Total nSV = 69 Accuracy = 97% (97/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 50 nu = 0.593814 obj = -15.451490, rho = -0.034362 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 43 nu = 0.517493 obj = -18.758767, rho = 0.006357 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 51 nu = 0.435660 obj = -22.442028, rho = 0.070756 nSV = 48, nBSV = 40 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 69 nu = 0.358535 obj = -26.751275, rho = 0.029778 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 49 nu = 0.299568 obj = -32.001963, rho = 0.006947 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.244240 obj = -38.010720, rho = -0.043988 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 91 nu = 0.204362 obj = -45.794757, rho = -0.136207 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 47 nu = 0.174533 obj = -54.815155, rho = -0.221067 nSV = 20, nBSV = 15 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 144 nu = 0.149364 obj = -63.092878, rho = -0.229995 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) ..*.* optimization finished, #iter = 339 nu = 0.116429 obj = -71.938449, rho = -0.248006 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -0.876911, rho = -0.944150 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -1.246830, rho = -0.919663 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -1.763372, rho = -0.884439 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 49 nu = 0.900000 obj = -2.474183, rho = -0.833480 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 49 nu = 0.900000 obj = -3.429992, rho = -0.760469 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 49 nu = 0.900000 obj = -4.666959, rho = -0.655447 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 64% (64/100) (classification) Accuracy = 61.2% (612/1000) (classification) * optimization finished, #iter = 52 nu = 0.895308 obj = -6.161192, rho = -0.510950 nSV = 91, nBSV = 87 Total nSV = 91 Accuracy = 95% (95/100) (classification) Accuracy = 84.9% (849/1000) (classification) * optimization finished, #iter = 49 nu = 0.856958 obj = -7.864582, rho = -0.378957 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 97% (97/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 52 nu = 0.773863 obj = -9.696235, rho = -0.333220 nSV = 79, nBSV = 75 Total nSV = 79 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 59 nu = 0.661913 obj = -11.720629, rho = -0.344005 nSV = 70, nBSV = 61 Total nSV = 70 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 36 nu = 0.547245 obj = -14.210477, rho = -0.382610 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 77 nu = 0.464827 obj = -17.196771, rho = -0.403170 nSV = 51, nBSV = 44 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 36 nu = 0.404150 obj = -20.653221, rho = -0.495803 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 55 nu = 0.329339 obj = -24.525158, rho = -0.488350 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 34 nu = 0.274183 obj = -29.176992, rho = -0.463906 nSV = 30, nBSV = 26 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 157 nu = 0.223921 obj = -34.661602, rho = -0.517143 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 172 nu = 0.182128 obj = -41.901254, rho = -0.602027 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 55 nu = 0.152236 obj = -50.851379, rho = -0.734402 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 73 nu = 0.125174 obj = -62.981827, rho = -0.697685 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 72 nu = 0.106997 obj = -79.195959, rho = -0.747673 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -0.932752, rho = -0.923920 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.324533, rho = -0.890562 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.869716, rho = -0.842579 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.615919, rho = -0.773558 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.610633, rho = -0.674274 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.878721, rho = -0.531460 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 71% (71/100) (classification) Accuracy = 72.6% (726/1000) (classification) * optimization finished, #iter = 48 nu = 0.954079 obj = -6.366587, rho = -0.346604 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 97% (97/100) (classification) Accuracy = 94.4% (944/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -8.048292, rho = -0.310846 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 53 nu = 0.801008 obj = -9.828357, rho = -0.205225 nSV = 82, nBSV = 79 Total nSV = 82 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 67 nu = 0.687337 obj = -11.659417, rho = -0.152166 nSV = 72, nBSV = 66 Total nSV = 72 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 35 nu = 0.563049 obj = -13.619966, rho = -0.183723 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 37 nu = 0.458083 obj = -15.907231, rho = -0.168846 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 37 nu = 0.372705 obj = -18.579312, rho = -0.226901 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 43 nu = 0.301988 obj = -21.793093, rho = -0.211561 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.246292 obj = -25.415719, rho = -0.287092 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 38 nu = 0.197136 obj = -29.716579, rho = -0.320344 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 40 nu = 0.170524 obj = -34.562423, rho = -0.220832 nSV = 20, nBSV = 15 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 73 nu = 0.135695 obj = -38.683769, rho = -0.198808 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.108287 obj = -42.963842, rho = -0.126046 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) *.........* optimization finished, #iter = 990 nu = 0.080796 obj = -47.201650, rho = -0.145913 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -0.916724, rho = -0.932278 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.303983, rho = -0.902585 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.845339, rho = -0.859873 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.591580, rho = -0.798435 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.597816, rho = -0.710059 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -4.906203, rho = -0.582934 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 68% (68/100) (classification) Accuracy = 64.9% (649/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -6.500574, rho = -0.400071 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 91% (91/100) (classification) Accuracy = 91.1% (911/1000) (classification) * optimization finished, #iter = 45 nu = 0.881317 obj = -8.300460, rho = -0.256581 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 94% (94/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 45 nu = 0.793828 obj = -10.431609, rho = -0.239505 nSV = 82, nBSV = 78 Total nSV = 82 Accuracy = 96% (96/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 57 nu = 0.713512 obj = -12.832198, rho = -0.125622 nSV = 74, nBSV = 69 Total nSV = 74 Accuracy = 96% (96/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 64 nu = 0.596658 obj = -15.593140, rho = -0.147307 nSV = 65, nBSV = 56 Total nSV = 65 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 0.505179 obj = -19.087187, rho = -0.174578 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 48 nu = 0.430307 obj = -23.306853, rho = -0.104317 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 33 nu = 0.371981 obj = -28.248239, rho = -0.217098 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 45 nu = 0.310015 obj = -33.958150, rho = -0.212949 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 88 nu = 0.259441 obj = -40.905733, rho = -0.288017 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 55 nu = 0.218879 obj = -49.653597, rho = -0.260576 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.179089 obj = -60.239241, rho = -0.294830 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 143 nu = 0.154969 obj = -73.886838, rho = -0.179751 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *..* optimization finished, #iter = 207 nu = 0.132959 obj = -89.503030, rho = -0.220301 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -0.924403, rho = 0.822078 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.307256, rho = 0.744067 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.833968, rho = 0.631854 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -2.541952, rho = 0.470440 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -3.457585, rho = 0.238255 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 73% (73/100) (classification) Accuracy = 65.8% (658/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.562043, rho = -0.095732 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 92% (92/100) (classification) Accuracy = 86.9% (869/1000) (classification) * optimization finished, #iter = 45 nu = 0.893674 obj = -5.799567, rho = -0.224828 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 95% (95/100) (classification) Accuracy = 92.3% (923/1000) (classification) * optimization finished, #iter = 44 nu = 0.794384 obj = -7.241592, rho = -0.240022 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 95% (95/100) (classification) Accuracy = 94.1% (941/1000) (classification) * optimization finished, #iter = 35 nu = 0.698772 obj = -8.977680, rho = -0.223255 nSV = 70, nBSV = 68 Total nSV = 70 Accuracy = 95% (95/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 39 nu = 0.612065 obj = -10.962845, rho = -0.154826 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 39 nu = 0.521709 obj = -13.257351, rho = -0.170835 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 45 nu = 0.435792 obj = -15.941234, rho = -0.142699 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 32 nu = 0.362159 obj = -19.236044, rho = -0.138505 nSV = 39, nBSV = 35 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 42 nu = 0.301839 obj = -23.245968, rho = -0.143603 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 97 nu = 0.255154 obj = -28.179495, rho = -0.034679 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 58 nu = 0.210991 obj = -34.448081, rho = -0.035524 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 25 nu = 0.186909 obj = -41.987177, rho = 0.021353 nSV = 21, nBSV = 17 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 59 nu = 0.157318 obj = -49.751989, rho = 0.045496 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.130806 obj = -59.355280, rho = 0.076115 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 46 nu = 0.106283 obj = -71.103436, rho = 0.010580 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -0.890917, rho = -0.917767 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.263197, rho = -0.881711 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.779094, rho = -0.829848 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -2.480608, rho = -0.755245 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -3.405742, rho = -0.647932 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 57% (57/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -4.562780, rho = -0.493567 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 84% (84/100) (classification) Accuracy = 74.7% (747/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -5.873430, rho = -0.335562 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 95% (95/100) (classification) Accuracy = 93.3% (933/1000) (classification) * optimization finished, #iter = 44 nu = 0.809595 obj = -7.360765, rho = -0.318776 nSV = 82, nBSV = 79 Total nSV = 82 Accuracy = 97% (97/100) (classification) Accuracy = 94.7% (947/1000) (classification) * optimization finished, #iter = 36 nu = 0.720000 obj = -9.086276, rho = -0.243570 nSV = 72, nBSV = 72 Total nSV = 72 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 45 nu = 0.619475 obj = -10.969877, rho = -0.190313 nSV = 63, nBSV = 60 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 51 nu = 0.514985 obj = -13.208074, rho = -0.197625 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 86 nu = 0.431702 obj = -15.919262, rho = -0.220418 nSV = 48, nBSV = 40 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 62 nu = 0.361298 obj = -19.208865, rho = -0.208291 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.301800 obj = -23.152806, rho = -0.155407 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 51 nu = 0.249327 obj = -28.275122, rho = -0.149239 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.216705 obj = -34.365190, rho = -0.182042 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 87 nu = 0.178782 obj = -41.907253, rho = -0.207235 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 176 nu = 0.151835 obj = -51.596164, rho = -0.264571 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 96 nu = 0.134903 obj = -62.713238, rho = -0.261853 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 162 nu = 0.116848 obj = -74.526807, rho = -0.004594 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -0.924745, rho = -0.915389 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.307964, rho = -0.878292 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -1.835434, rho = -0.824167 nSV = 97, nBSV = 94 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -2.544985, rho = -0.747072 nSV = 97, nBSV = 94 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -3.463863, rho = -0.636176 nSV = 97, nBSV = 94 Total nSV = 97 Accuracy = 76% (76/100) (classification) Accuracy = 68.3% (683/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -4.575032, rho = -0.476658 nSV = 97, nBSV = 94 Total nSV = 97 Accuracy = 90% (90/100) (classification) Accuracy = 86.7% (867/1000) (classification) * optimization finished, #iter = 50 nu = 0.899459 obj = -5.804326, rho = -0.385248 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 94% (94/100) (classification) Accuracy = 89.7% (897/1000) (classification) * optimization finished, #iter = 42 nu = 0.800000 obj = -7.261819, rho = -0.389884 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 95% (95/100) (classification) Accuracy = 93.6% (936/1000) (classification) * optimization finished, #iter = 57 nu = 0.696018 obj = -8.975057, rho = -0.429404 nSV = 71, nBSV = 66 Total nSV = 71 Accuracy = 96% (96/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 43 nu = 0.606703 obj = -11.029780, rho = -0.415684 nSV = 62, nBSV = 59 Total nSV = 62 Accuracy = 97% (97/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 49 nu = 0.520295 obj = -13.409361, rho = -0.379609 nSV = 56, nBSV = 49 Total nSV = 56 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 36 nu = 0.434392 obj = -16.314684, rho = -0.341748 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 42 nu = 0.370475 obj = -19.973993, rho = -0.333608 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 42 nu = 0.314815 obj = -24.259485, rho = -0.351372 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 35 nu = 0.265779 obj = -29.395052, rho = -0.376443 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 0.223905 obj = -35.436498, rho = -0.330726 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 64 nu = 0.195730 obj = -42.197056, rho = -0.190025 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 54 nu = 0.162331 obj = -49.590221, rho = -0.155583 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.129253 obj = -57.998572, rho = -0.177943 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *..* optimization finished, #iter = 267 nu = 0.106173 obj = -67.917981, rho = -0.136408 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.971154, rho = -0.020068 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 93% (93/100) (classification) Accuracy = 93.9% (939/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.378763, rho = -0.028867 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 93% (93/100) (classification) Accuracy = 93.9% (939/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.945638, rho = -0.041523 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 93% (93/100) (classification) Accuracy = 93.9% (939/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.720814, rho = -0.059729 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 93% (93/100) (classification) Accuracy = 93.9% (939/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.752589, rho = -0.085917 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 93% (93/100) (classification) Accuracy = 93.9% (939/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -5.064440, rho = -0.123587 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 93% (93/100) (classification) Accuracy = 93.9% (939/1000) (classification) * optimization finished, #iter = 50 nu = 0.979672 obj = -6.607896, rho = -0.200142 nSV = 98, nBSV = 96 Total nSV = 98 Accuracy = 96% (96/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 49 nu = 0.897039 obj = -8.438004, rho = -0.195350 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 44 nu = 0.806762 obj = -10.578506, rho = -0.222351 nSV = 82, nBSV = 79 Total nSV = 82 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 48 nu = 0.713131 obj = -13.033492, rho = -0.185336 nSV = 73, nBSV = 68 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 48 nu = 0.612013 obj = -15.971212, rho = -0.121301 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 53 nu = 0.525353 obj = -19.345068, rho = -0.107791 nSV = 56, nBSV = 49 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.436773 obj = -23.313703, rho = -0.130816 nSV = 48, nBSV = 40 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 54 nu = 0.367627 obj = -28.296625, rho = -0.104159 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 79 nu = 0.307138 obj = -34.501270, rho = -0.084833 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 51 nu = 0.258011 obj = -42.499976, rho = -0.095740 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 30 nu = 0.227685 obj = -52.032564, rho = 0.051466 nSV = 25, nBSV = 21 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 44 nu = 0.195940 obj = -62.441782, rho = -0.084761 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 61 nu = 0.161152 obj = -75.140291, rho = -0.042842 nSV = 19, nBSV = 14 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 69 nu = 0.136186 obj = -89.887473, rho = -0.084504 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -0.929484, rho = -0.920423 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.317771, rho = -0.885532 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.855725, rho = -0.835343 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.586969, rho = -0.763150 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.550732, rho = -0.659303 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 58% (58/100) (classification) Accuracy = 53.4% (534/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.754776, rho = -0.509924 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 86% (86/100) (classification) Accuracy = 78.7% (787/1000) (classification) * optimization finished, #iter = 51 nu = 0.932753 obj = -6.132873, rho = -0.377372 nSV = 94, nBSV = 90 Total nSV = 94 Accuracy = 96% (96/100) (classification) Accuracy = 93.7% (937/1000) (classification) * optimization finished, #iter = 44 nu = 0.824562 obj = -7.772580, rho = -0.316600 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 97% (97/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 38 nu = 0.744808 obj = -9.722499, rho = -0.270639 nSV = 76, nBSV = 74 Total nSV = 76 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 55 nu = 0.664328 obj = -11.878643, rho = -0.310585 nSV = 69, nBSV = 63 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 33 nu = 0.560917 obj = -14.404323, rho = -0.289814 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 38 nu = 0.477556 obj = -17.294468, rho = -0.247371 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 64 nu = 0.398850 obj = -20.683959, rho = -0.195967 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 60 nu = 0.324743 obj = -24.868815, rho = -0.174253 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 88 nu = 0.277418 obj = -29.923710, rho = -0.192900 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 46 nu = 0.231667 obj = -35.830401, rho = -0.220549 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.190689 obj = -42.783051, rho = -0.245725 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 84 nu = 0.160119 obj = -51.429138, rho = -0.467643 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 74 nu = 0.135268 obj = -61.672989, rho = -0.468891 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 88 nu = 0.110891 obj = -73.874383, rho = -0.494908 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -0.900087, rho = -0.935044 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -1.282174, rho = -0.906551 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -1.818358, rho = -0.865578 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -2.561854, rho = -0.806724 nSV = 93, nBSV = 90 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -3.573852, rho = -0.721982 nSV = 93, nBSV = 90 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 50 nu = 0.920000 obj = -4.910623, rho = -0.600143 nSV = 94, nBSV = 90 Total nSV = 94 Accuracy = 61% (61/100) (classification) Accuracy = 58.2% (582/1000) (classification) * optimization finished, #iter = 50 nu = 0.920000 obj = -6.587407, rho = -0.424661 nSV = 94, nBSV = 90 Total nSV = 94 Accuracy = 84% (84/100) (classification) Accuracy = 86.1% (861/1000) (classification) * optimization finished, #iter = 46 nu = 0.884921 obj = -8.530480, rho = -0.245925 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 98% (98/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 43 nu = 0.795340 obj = -10.838272, rho = -0.166889 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 97% (97/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 39 nu = 0.721079 obj = -13.631186, rho = -0.219757 nSV = 74, nBSV = 71 Total nSV = 74 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 38 nu = 0.640303 obj = -16.807598, rho = -0.089587 nSV = 66, nBSV = 63 Total nSV = 66 Accuracy = 96% (96/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 37 nu = 0.545726 obj = -20.587542, rho = -0.084207 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 96% (96/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 43 nu = 0.465572 obj = -25.180333, rho = -0.062294 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 96% (96/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 54 nu = 0.392764 obj = -30.933194, rho = -0.103823 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 96% (96/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 65 nu = 0.336152 obj = -37.967955, rho = -0.082371 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 91 nu = 0.288789 obj = -46.389540, rho = 0.040861 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 130 nu = 0.246532 obj = -56.235145, rho = 0.017247 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 93 nu = 0.202029 obj = -68.752757, rho = -0.004723 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.176008 obj = -84.455068, rho = -0.047588 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 97 nu = 0.150938 obj = -103.092821, rho = 0.011824 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -0.804793, rho = 0.933575 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 46.2% (462/1000) (classification) * optimization finished, #iter = 46 nu = 0.820000 obj = -1.148064, rho = 0.904334 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 59% (59/100) (classification) Accuracy = 46.2% (462/1000) (classification) * optimization finished, #iter = 46 nu = 0.820000 obj = -1.631588, rho = 0.862389 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 59% (59/100) (classification) Accuracy = 46.2% (462/1000) (classification) * optimization finished, #iter = 46 nu = 0.820000 obj = -2.305897, rho = 0.802053 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 59% (59/100) (classification) Accuracy = 46.2% (462/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -3.231956, rho = 0.715263 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 59% (59/100) (classification) Accuracy = 46.2% (462/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -4.473212, rho = 0.590420 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 59% (59/100) (classification) Accuracy = 46.2% (462/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -6.070745, rho = 0.410840 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 66% (66/100) (classification) Accuracy = 58.6% (586/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -7.979823, rho = 0.152523 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 85% (85/100) (classification) Accuracy = 90.8% (908/1000) (classification) * optimization finished, #iter = 42 nu = 0.766119 obj = -10.119108, rho = 0.002967 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 97% (97/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 45 nu = 0.685447 obj = -12.472837, rho = -0.093407 nSV = 71, nBSV = 66 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 45 nu = 0.589168 obj = -15.187050, rho = -0.108275 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 39 nu = 0.504590 obj = -18.358300, rho = -0.157496 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 34 nu = 0.426756 obj = -21.872399, rho = -0.168201 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 33 nu = 0.355570 obj = -25.686129, rho = -0.272328 nSV = 38, nBSV = 34 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 64 nu = 0.293519 obj = -29.701055, rho = -0.320001 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 140 nu = 0.236526 obj = -34.137415, rho = -0.271753 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 57 nu = 0.188016 obj = -39.310551, rho = -0.336498 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 45 nu = 0.148414 obj = -45.339903, rho = -0.401749 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 59 nu = 0.120968 obj = -52.897750, rho = -0.501515 nSV = 14, nBSV = 9 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 152 nu = 0.098011 obj = -60.619834, rho = -0.611950 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -0.839092, rho = -0.940046 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -1.193806, rho = -0.913760 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -1.689945, rho = -0.875948 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -2.374447, rho = -0.821557 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -3.298710, rho = -0.743319 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -4.503326, rho = -0.630777 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 69% (69/100) (classification) Accuracy = 57% (570/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -5.977693, rho = -0.468891 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 92% (92/100) (classification) Accuracy = 83.7% (837/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -7.630391, rho = -0.346609 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 98% (98/100) (classification) Accuracy = 94.4% (944/1000) (classification) * optimization finished, #iter = 44 nu = 0.730220 obj = -9.455681, rho = -0.315844 nSV = 76, nBSV = 71 Total nSV = 76 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 47 nu = 0.637308 obj = -11.628403, rho = -0.341768 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 59 nu = 0.551170 obj = -14.062840, rho = -0.287023 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 33 nu = 0.459412 obj = -17.059843, rho = -0.300889 nSV = 47, nBSV = 44 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 26 nu = 0.396192 obj = -20.615963, rho = -0.443115 nSV = 41, nBSV = 37 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 24 nu = 0.328320 obj = -24.660005, rho = -0.452417 nSV = 35, nBSV = 31 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 71 nu = 0.283391 obj = -29.118390, rho = -0.451926 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 86 nu = 0.230870 obj = -34.060479, rho = -0.408209 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 39 nu = 0.188618 obj = -39.473198, rho = -0.529644 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 85 nu = 0.149256 obj = -45.576548, rho = -0.604750 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 83 nu = 0.119498 obj = -53.192509, rho = -0.593777 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 88 nu = 0.096359 obj = -62.849369, rho = -0.552370 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -0.952142, rho = 0.864919 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.352038, rho = 0.805693 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.908484, rho = 0.720500 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -2.670035, rho = 0.597953 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -3.685065, rho = 0.421675 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 52% (52/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -4.978726, rho = 0.168109 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 78% (78/100) (classification) Accuracy = 77.8% (778/1000) (classification) * optimization finished, #iter = 49 nu = 0.970924 obj = -6.496409, rho = -0.137726 nSV = 98, nBSV = 96 Total nSV = 98 Accuracy = 95% (95/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 51 nu = 0.895878 obj = -8.217919, rho = -0.147579 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 52 nu = 0.790531 obj = -10.185626, rho = -0.105435 nSV = 81, nBSV = 77 Total nSV = 81 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 45 nu = 0.680000 obj = -12.549045, rho = -0.139333 nSV = 69, nBSV = 67 Total nSV = 69 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 33 nu = 0.597070 obj = -15.300709, rho = -0.177599 nSV = 60, nBSV = 58 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 58 nu = 0.496793 obj = -18.483287, rho = -0.174840 nSV = 53, nBSV = 46 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 37 nu = 0.417617 obj = -22.609284, rho = -0.149198 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 43 nu = 0.354728 obj = -27.679816, rho = -0.141940 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 43 nu = 0.305012 obj = -33.927914, rho = -0.126892 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 62 nu = 0.261967 obj = -41.139107, rho = -0.033437 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 41 nu = 0.221396 obj = -49.376943, rho = 0.139005 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 98 nu = 0.186909 obj = -58.292659, rho = 0.142335 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 140 nu = 0.154156 obj = -68.735275, rho = -0.003346 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.125426 obj = -81.604563, rho = 0.083827 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.968193, rho = -0.046585 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 95% (95/100) (classification) Accuracy = 92.8% (928/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.372636, rho = -0.067009 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 95% (95/100) (classification) Accuracy = 92.8% (928/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.932961, rho = -0.096390 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 95% (95/100) (classification) Accuracy = 92.8% (928/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.694582, rho = -0.138652 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 95% (95/100) (classification) Accuracy = 92.8% (928/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.698313, rho = -0.199444 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 95% (95/100) (classification) Accuracy = 92.8% (928/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -4.952135, rho = -0.286890 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 95% (95/100) (classification) Accuracy = 92.8% (928/1000) (classification) * optimization finished, #iter = 49 nu = 0.953025 obj = -6.453260, rho = -0.230878 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 97% (97/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -8.234778, rho = -0.266849 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 45 nu = 0.806413 obj = -10.199261, rho = -0.217802 nSV = 82, nBSV = 79 Total nSV = 82 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.699492 obj = -12.360321, rho = -0.156522 nSV = 72, nBSV = 67 Total nSV = 72 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 49 nu = 0.588588 obj = -14.766681, rho = -0.101716 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 41 nu = 0.493725 obj = -17.545417, rho = -0.168954 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 51 nu = 0.407636 obj = -20.755797, rho = -0.130280 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 55 nu = 0.334249 obj = -24.436445, rho = -0.167507 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 37 nu = 0.274175 obj = -28.915405, rho = -0.220077 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 42 nu = 0.222583 obj = -34.292824, rho = -0.214106 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 42 nu = 0.186258 obj = -40.982274, rho = -0.139716 nSV = 22, nBSV = 17 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 75 nu = 0.165403 obj = -47.181348, rho = -0.040128 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.130266 obj = -52.791952, rho = -0.036991 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 130 nu = 0.098984 obj = -59.171298, rho = -0.041716 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -0.897209, rho = -0.938199 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.276217, rho = -0.911102 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.806033, rho = -0.872124 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -2.536349, rho = -0.816057 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -3.521079, rho = -0.735407 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -4.801427, rho = -0.619397 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 58% (58/100) (classification) Accuracy = 55% (550/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -6.361460, rho = -0.452521 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 88% (88/100) (classification) Accuracy = 85.4% (854/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -8.100263, rho = -0.270356 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 97% (97/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 49 nu = 0.798204 obj = -9.968791, rho = -0.194002 nSV = 82, nBSV = 77 Total nSV = 82 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 45 nu = 0.674619 obj = -12.158061, rho = -0.200272 nSV = 69, nBSV = 66 Total nSV = 69 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 65 nu = 0.568486 obj = -14.782032, rho = -0.165384 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 40 nu = 0.480475 obj = -18.006917, rho = -0.104814 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 55 nu = 0.409968 obj = -21.781810, rho = -0.112201 nSV = 45, nBSV = 37 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 44 nu = 0.345829 obj = -26.326115, rho = -0.114069 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.292277 obj = -31.660556, rho = -0.117272 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 87 nu = 0.245093 obj = -37.699969, rho = -0.183353 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.200532 obj = -45.234935, rho = -0.200017 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 53 nu = 0.163359 obj = -54.943150, rho = -0.247793 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 77 nu = 0.140000 obj = -67.847314, rho = -0.374450 nSV = 17, nBSV = 12 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 61 nu = 0.125273 obj = -82.285365, rho = -0.715773 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -0.913302, rho = -0.918457 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.296902, rho = -0.882704 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.830689, rho = -0.831275 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.561265, rho = -0.757298 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.535091, rho = -0.650885 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 51.1% (511/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -4.776417, rho = -0.497816 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 72% (72/100) (classification) Accuracy = 67.7% (677/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -6.232029, rho = -0.277634 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 96% (96/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -7.772023, rho = -0.148904 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 43 nu = 0.755215 obj = -9.557941, rho = -0.121529 nSV = 77, nBSV = 74 Total nSV = 77 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 36 nu = 0.640905 obj = -11.672857, rho = -0.211563 nSV = 66, nBSV = 63 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 33 nu = 0.565074 obj = -14.103203, rho = -0.153087 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 31 nu = 0.479823 obj = -16.636638, rho = -0.105838 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 58 nu = 0.384148 obj = -19.526883, rho = -0.099553 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 46 nu = 0.310627 obj = -23.155261, rho = -0.095167 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 63 nu = 0.251038 obj = -27.816805, rho = -0.108975 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 50 nu = 0.214976 obj = -33.858459, rho = -0.081004 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 73 nu = 0.181480 obj = -40.906674, rho = -0.076456 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 56 nu = 0.149472 obj = -49.463042, rho = -0.100826 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 62 nu = 0.132766 obj = -58.966171, rho = 0.002313 nSV = 15, nBSV = 11 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 137 nu = 0.108617 obj = -68.897590, rho = 0.139665 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.948768, rho = -0.913882 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.345058, rho = -0.876124 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.894042, rho = -0.821811 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.640153, rho = -0.743683 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.623234, rho = -0.631301 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 59% (59/100) (classification) Accuracy = 54.7% (547/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.850789, rho = -0.469646 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 90% (90/100) (classification) Accuracy = 87% (870/1000) (classification) * optimization finished, #iter = 50 nu = 0.942906 obj = -6.267451, rho = -0.377921 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 100% (100/100) (classification) Accuracy = 94.8% (948/1000) (classification) * optimization finished, #iter = 47 nu = 0.866156 obj = -7.891963, rho = -0.322425 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 40 nu = 0.780000 obj = -9.676645, rho = -0.269700 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.671569 obj = -11.618769, rho = -0.331866 nSV = 69, nBSV = 66 Total nSV = 69 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 42 nu = 0.563832 obj = -13.744253, rho = -0.319452 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 34 nu = 0.464137 obj = -16.137912, rho = -0.300841 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 45 nu = 0.380108 obj = -18.753330, rho = -0.357619 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.302981 obj = -21.737687, rho = -0.398957 nSV = 33, nBSV = 29 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 65 nu = 0.246871 obj = -25.238109, rho = -0.375092 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 77 nu = 0.201419 obj = -29.254716, rho = -0.508306 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.156926 obj = -34.139404, rho = -0.541774 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 90 nu = 0.128890 obj = -40.004078, rho = -0.524217 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 56 nu = 0.105367 obj = -46.634781, rho = -0.716743 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 37 nu = 0.090328 obj = -53.341712, rho = -0.703824 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -0.818199, rho = 0.885347 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 44 nu = 0.840000 obj = -1.163190, rho = 0.835624 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 44 nu = 0.840000 obj = -1.644741, rho = 0.763554 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 45 nu = 0.840000 obj = -2.307014, rho = 0.659628 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 58% (58/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 47 nu = 0.840000 obj = -3.196729, rho = 0.510358 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 58% (58/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 47 nu = 0.840000 obj = -4.346318, rho = 0.295675 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 72% (72/100) (classification) Accuracy = 63.3% (633/1000) (classification) * optimization finished, #iter = 44 nu = 0.840000 obj = -5.730504, rho = -0.013532 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 88% (88/100) (classification) Accuracy = 88.2% (882/1000) (classification) * optimization finished, #iter = 42 nu = 0.787950 obj = -7.275078, rho = -0.090689 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 93% (93/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 38 nu = 0.699060 obj = -9.067545, rho = -0.127531 nSV = 70, nBSV = 67 Total nSV = 70 Accuracy = 95% (95/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 39 nu = 0.607442 obj = -11.211991, rho = -0.100869 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 95% (95/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 42 nu = 0.532112 obj = -13.662992, rho = -0.176551 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 54 nu = 0.444236 obj = -16.523774, rho = -0.207543 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 97% (97/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 59 nu = 0.379977 obj = -19.951620, rho = -0.220026 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 97% (97/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 60 nu = 0.318209 obj = -23.941948, rho = -0.242362 nSV = 35, nBSV = 30 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 91 nu = 0.261380 obj = -28.770251, rho = -0.246881 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.212812 obj = -35.115345, rho = -0.214826 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 73 nu = 0.178191 obj = -43.754693, rho = -0.180817 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.156827 obj = -54.902559, rho = -0.018777 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.132686 obj = -69.561820, rho = 0.009479 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.117323 obj = -88.730558, rho = -0.047782 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -0.932359, rho = 0.869055 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 52.7% (527/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.323718, rho = 0.811642 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 52.7% (527/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.868032, rho = 0.729056 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 52.7% (527/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.612433, rho = 0.610261 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 52.7% (527/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.603422, rho = 0.439380 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 53.4% (534/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.863799, rho = 0.193577 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 78% (78/100) (classification) Accuracy = 78.5% (785/1000) (classification) * optimization finished, #iter = 50 nu = 0.953598 obj = -6.335671, rho = -0.131221 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -7.968525, rho = -0.248747 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 42 nu = 0.766611 obj = -9.838444, rho = -0.222477 nSV = 80, nBSV = 76 Total nSV = 80 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 40 nu = 0.654191 obj = -12.114238, rho = -0.239990 nSV = 69, nBSV = 64 Total nSV = 69 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 37 nu = 0.571350 obj = -14.865366, rho = -0.232634 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 43 nu = 0.497591 obj = -17.932342, rho = -0.159073 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 51 nu = 0.418179 obj = -21.297997, rho = -0.121985 nSV = 46, nBSV = 38 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 88 nu = 0.343391 obj = -25.200707, rho = -0.111041 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 91 nu = 0.278075 obj = -29.895092, rho = -0.120735 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 82 nu = 0.228416 obj = -35.794158, rho = -0.059098 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 61 nu = 0.191425 obj = -42.742720, rho = 0.040881 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 76 nu = 0.158144 obj = -50.934976, rho = 0.003324 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.131878 obj = -61.539637, rho = -0.095665 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.106230 obj = -74.808499, rho = -0.122372 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -0.912443, rho = 0.847316 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.295125, rho = 0.780372 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.827011, rho = 0.684077 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.553656, rho = 0.545560 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -3.519346, rho = 0.346311 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 55% (55/100) (classification) Accuracy = 53% (530/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.743838, rho = 0.059701 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 82% (82/100) (classification) Accuracy = 79.2% (792/1000) (classification) * optimization finished, #iter = 48 nu = 0.919773 obj = -6.170183, rho = -0.233986 nSV = 93, nBSV = 90 Total nSV = 93 Accuracy = 91% (91/100) (classification) Accuracy = 93.6% (936/1000) (classification) * optimization finished, #iter = 42 nu = 0.834250 obj = -7.846449, rho = -0.236640 nSV = 84, nBSV = 82 Total nSV = 84 Accuracy = 95% (95/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 42 nu = 0.748722 obj = -9.843088, rho = -0.256193 nSV = 78, nBSV = 74 Total nSV = 78 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 56 nu = 0.655593 obj = -12.185448, rho = -0.197790 nSV = 68, nBSV = 62 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 37 nu = 0.573199 obj = -15.029599, rho = -0.231813 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 46 nu = 0.486918 obj = -18.382177, rho = -0.250133 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 41 nu = 0.421286 obj = -22.226507, rho = -0.209290 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 61 nu = 0.355286 obj = -26.660848, rho = -0.221350 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 65 nu = 0.292240 obj = -31.957875, rho = -0.226982 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 67 nu = 0.244564 obj = -38.435894, rho = -0.228803 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 56 nu = 0.203648 obj = -46.528302, rho = -0.188974 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 98 nu = 0.171387 obj = -56.586052, rho = -0.189252 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.144008 obj = -68.491792, rho = -0.340480 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 69 nu = 0.120288 obj = -83.963514, rho = -0.386358 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.953819, rho = -0.905285 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.355509, rho = -0.863757 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.915667, rho = -0.804021 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.684898, rho = -0.718094 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.715817, rho = -0.594493 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.8% (518/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -5.042357, rho = -0.416698 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 68% (68/100) (classification) Accuracy = 71.9% (719/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -6.626933, rho = -0.160949 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 46 nu = 0.910183 obj = -8.402477, rho = -0.100663 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 54 nu = 0.811657 obj = -10.413627, rho = -0.102413 nSV = 83, nBSV = 79 Total nSV = 83 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 46 nu = 0.707544 obj = -12.766691, rho = -0.105446 nSV = 74, nBSV = 68 Total nSV = 74 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 46 nu = 0.605182 obj = -15.447196, rho = -0.045203 nSV = 63, nBSV = 57 Total nSV = 63 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 32 nu = 0.510572 obj = -18.531189, rho = -0.016829 nSV = 53, nBSV = 50 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 45 nu = 0.427273 obj = -22.090900, rho = -0.019390 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 61 nu = 0.358879 obj = -25.990941, rho = 0.046401 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 60 nu = 0.291887 obj = -30.433733, rho = 0.105970 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 75 nu = 0.240024 obj = -35.537514, rho = 0.047518 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 83 nu = 0.194128 obj = -41.543138, rho = 0.141477 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 83 nu = 0.165744 obj = -48.251492, rho = 0.135889 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 145 nu = 0.132393 obj = -53.659855, rho = 0.040021 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*..* optimization finished, #iter = 438 nu = 0.105179 obj = -57.647025, rho = 0.086917 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.948742, rho = 0.812550 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.345004, rho = 0.730363 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.893929, rho = 0.612141 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.639920, rho = 0.442084 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.622753, rho = 0.197466 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 60% (60/100) (classification) Accuracy = 63% (630/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.849794, rho = -0.154405 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 90% (90/100) (classification) Accuracy = 89.7% (897/1000) (classification) * optimization finished, #iter = 50 nu = 0.929261 obj = -6.282618, rho = -0.288189 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 96% (96/100) (classification) Accuracy = 94.8% (948/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -7.996566, rho = -0.312130 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 97% (97/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 43 nu = 0.766115 obj = -9.967437, rho = -0.276511 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 38 nu = 0.680090 obj = -12.238284, rho = -0.277485 nSV = 70, nBSV = 67 Total nSV = 70 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 43 nu = 0.577238 obj = -14.804447, rho = -0.245739 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 51 nu = 0.489734 obj = -17.813679, rho = -0.328342 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.411937 obj = -21.302888, rho = -0.407818 nSV = 45, nBSV = 37 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 57 nu = 0.340050 obj = -25.489835, rho = -0.393150 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 60 nu = 0.285262 obj = -30.473598, rho = -0.376012 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 74 nu = 0.236995 obj = -36.278308, rho = -0.398732 nSV = 26, nBSV = 21 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.196254 obj = -43.156053, rho = -0.363288 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .**.* optimization finished, #iter = 213 nu = 0.156044 obj = -51.826880, rho = -0.348378 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 161 nu = 0.131184 obj = -63.819702, rho = -0.344592 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 96 nu = 0.111924 obj = -78.382865, rho = -0.328134 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -0.901005, rho = 0.918534 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -1.284072, rho = 0.882815 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -1.822285, rho = 0.831435 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -2.569977, rho = 0.757528 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -3.590660, rho = 0.651216 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -4.945400, rho = 0.498292 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 56% (56/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -6.659361, rho = 0.278319 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 82% (82/100) (classification) Accuracy = 82% (820/1000) (classification) * optimization finished, #iter = 45 nu = 0.899591 obj = -8.652740, rho = 0.027908 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 43 nu = 0.820000 obj = -10.955616, rho = -0.005135 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 55 nu = 0.722532 obj = -13.675354, rho = 0.006809 nSV = 76, nBSV = 70 Total nSV = 76 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 43 nu = 0.639542 obj = -16.903010, rho = 0.003441 nSV = 67, nBSV = 61 Total nSV = 67 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 86 nu = 0.551708 obj = -20.663153, rho = 0.047658 nSV = 58, nBSV = 50 Total nSV = 58 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 36 nu = 0.463516 obj = -25.265835, rho = 0.079157 nSV = 48, nBSV = 45 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 41 nu = 0.394203 obj = -31.067712, rho = 0.102472 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 85 nu = 0.334804 obj = -38.031463, rho = 0.090626 nSV = 39, nBSV = 30 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.287121 obj = -46.706997, rho = 0.116256 nSV = 34, nBSV = 25 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 63 nu = 0.247156 obj = -57.298528, rho = 0.191423 nSV = 30, nBSV = 21 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 69 nu = 0.213040 obj = -69.487879, rho = 0.207776 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 176 nu = 0.178790 obj = -84.203540, rho = 0.172070 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..* optimization finished, #iter = 279 nu = 0.146685 obj = -102.661940, rho = 0.156244 nSV = 21, nBSV = 10 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.953678, rho = 0.872021 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.355216, rho = 0.815908 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.915060, rho = 0.735193 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.683643, rho = 0.619088 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.713222, rho = 0.452078 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -5.036986, rho = 0.211841 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 78% (78/100) (classification) Accuracy = 81.1% (811/1000) (classification) * optimization finished, #iter = 52 nu = 0.974813 obj = -6.616160, rho = -0.109934 nSV = 98, nBSV = 96 Total nSV = 98 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -8.456685, rho = -0.087629 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 51 nu = 0.815674 obj = -10.554909, rho = -0.146070 nSV = 83, nBSV = 78 Total nSV = 83 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 41 nu = 0.714852 obj = -13.039942, rho = -0.104408 nSV = 73, nBSV = 70 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 53 nu = 0.613802 obj = -15.874917, rho = -0.054494 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 66 nu = 0.516108 obj = -19.331885, rho = -0.047671 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 63 nu = 0.449739 obj = -23.393407, rho = 0.072946 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 63 nu = 0.372442 obj = -27.974218, rho = 0.115944 nSV = 41, nBSV = 32 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.310122 obj = -33.680598, rho = 0.041401 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 89 nu = 0.255239 obj = -40.899151, rho = 0.101524 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 63 nu = 0.218930 obj = -49.488845, rho = 0.261550 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 50 nu = 0.192135 obj = -59.018605, rho = 0.270662 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 58 nu = 0.156132 obj = -68.585394, rho = 0.349232 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.127383 obj = -78.961344, rho = 0.273599 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.972743, rho = -0.015131 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.382052, rho = -0.021765 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.952443, rho = -0.031307 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.734894, rho = -0.045034 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.781723, rho = -0.064779 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -5.124722, rho = -0.093181 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 50 nu = 0.986389 obj = -6.722283, rho = -0.116841 nSV = 100, nBSV = 98 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 47 nu = 0.930383 obj = -8.506907, rho = -0.109519 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 97% (97/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -10.531909, rho = -0.044437 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 43 nu = 0.710162 obj = -12.892514, rho = -0.008855 nSV = 73, nBSV = 69 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 48 nu = 0.602218 obj = -15.666768, rho = -0.027596 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 87 nu = 0.517895 obj = -18.973331, rho = -0.068351 nSV = 57, nBSV = 50 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 35 nu = 0.435818 obj = -22.913219, rho = -0.038186 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 47 nu = 0.367701 obj = -27.355369, rho = -0.034193 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 174 nu = 0.300160 obj = -32.665762, rho = -0.093457 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 54 nu = 0.250894 obj = -39.188769, rho = -0.079467 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.207986 obj = -47.103482, rho = -0.162024 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.172183 obj = -57.346883, rho = -0.170174 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 95 nu = 0.150667 obj = -69.258172, rho = -0.058966 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 160 nu = 0.126530 obj = -82.084926, rho = -0.062745 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -0.859356, rho = -0.949258 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 48 nu = 0.880000 obj = -1.223123, rho = -0.927621 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 48 nu = 0.880000 obj = -1.732462, rho = -0.895886 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 48 nu = 0.880000 obj = -2.436319, rho = -0.850237 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 48 nu = 0.880000 obj = -3.389189, rho = -0.784574 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 48 nu = 0.880000 obj = -4.636536, rho = -0.690120 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 61% (61/100) (classification) Accuracy = 57.4% (574/1000) (classification) * optimization finished, #iter = 48 nu = 0.880000 obj = -6.175641, rho = -0.554254 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 82% (82/100) (classification) Accuracy = 83.5% (835/1000) (classification) * optimization finished, #iter = 51 nu = 0.852888 obj = -7.906422, rho = -0.377600 nSV = 88, nBSV = 84 Total nSV = 88 Accuracy = 95% (95/100) (classification) Accuracy = 94.6% (946/1000) (classification) * optimization finished, #iter = 54 nu = 0.759714 obj = -9.863602, rho = -0.299034 nSV = 78, nBSV = 74 Total nSV = 78 Accuracy = 97% (97/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 41 nu = 0.661402 obj = -12.163150, rho = -0.279135 nSV = 68, nBSV = 64 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 67 nu = 0.570116 obj = -14.815088, rho = -0.257000 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 54 nu = 0.493237 obj = -17.975655, rho = -0.252720 nSV = 53, nBSV = 45 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 67 nu = 0.411208 obj = -21.587891, rho = -0.232727 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 42 nu = 0.341784 obj = -25.870038, rho = -0.246232 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.288238 obj = -30.959445, rho = -0.234144 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 92 nu = 0.243446 obj = -36.700841, rho = -0.211767 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.199265 obj = -43.028862, rho = -0.216907 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.165518 obj = -50.445272, rho = -0.252602 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..* optimization finished, #iter = 256 nu = 0.132720 obj = -58.467466, rho = -0.282623 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 134 nu = 0.110339 obj = -68.111682, rho = -0.275659 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -0.844096, rho = -0.942609 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -1.204160, rho = -0.917446 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -1.711370, rho = -0.881250 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -2.418776, rho = -0.829184 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -3.390433, rho = -0.754290 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -4.693115, rho = -0.646559 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -6.370392, rho = -0.491593 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 76% (76/100) (classification) Accuracy = 69.5% (695/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -8.376351, rho = -0.268682 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 96% (96/100) (classification) Accuracy = 93.6% (936/1000) (classification) * optimization finished, #iter = 42 nu = 0.800000 obj = -10.615018, rho = -0.252507 nSV = 80, nBSV = 80 Total nSV = 80 Accuracy = 98% (98/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 39 nu = 0.720000 obj = -13.196516, rho = -0.241212 nSV = 73, nBSV = 71 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 54 nu = 0.625184 obj = -15.991945, rho = -0.275160 nSV = 66, nBSV = 59 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 44 nu = 0.530615 obj = -19.205653, rho = -0.226038 nSV = 56, nBSV = 50 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 44 nu = 0.438222 obj = -23.007009, rho = -0.228709 nSV = 45, nBSV = 41 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.364081 obj = -27.688481, rho = -0.281888 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 56 nu = 0.310543 obj = -33.350533, rho = -0.187850 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 86 nu = 0.262139 obj = -39.813303, rho = -0.131775 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*..* optimization finished, #iter = 331 nu = 0.222350 obj = -46.636844, rho = -0.168011 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..*....* optimization finished, #iter = 656 nu = 0.177611 obj = -53.864912, rho = -0.255892 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 175 nu = 0.139353 obj = -62.833449, rho = -0.221534 nSV = 21, nBSV = 10 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.114451 obj = -74.638760, rho = -0.255470 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -0.861972, rho = 0.918516 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -1.228534, rho = 0.882789 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -1.743659, rho = 0.831398 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -2.459489, rho = 0.757474 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -3.437130, rho = 0.651139 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -4.735733, rho = 0.498181 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 52.5% (525/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -6.380892, rho = 0.278159 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 77% (77/100) (classification) Accuracy = 74.9% (749/1000) (classification) * optimization finished, #iter = 44 nu = 0.874701 obj = -8.286870, rho = -0.019386 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 97% (97/100) (classification) Accuracy = 94.5% (945/1000) (classification) * optimization finished, #iter = 45 nu = 0.791633 obj = -10.408201, rho = -0.096199 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 99% (99/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 38 nu = 0.691658 obj = -12.912993, rho = -0.085981 nSV = 71, nBSV = 68 Total nSV = 71 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 44 nu = 0.621070 obj = -15.765394, rho = 0.033032 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 39 nu = 0.533346 obj = -18.726138, rho = 0.007554 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 46 nu = 0.440000 obj = -21.908202, rho = 0.001036 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 57 nu = 0.358882 obj = -25.359005, rho = -0.099590 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.291822 obj = -29.203203, rho = -0.067675 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 69 nu = 0.237042 obj = -33.487071, rho = -0.111543 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.186868 obj = -37.872079, rho = -0.119349 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.150592 obj = -42.586982, rho = -0.202736 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 138 nu = 0.119461 obj = -46.785739, rho = -0.201106 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 146 nu = 0.093487 obj = -50.093912, rho = -0.163256 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -0.859777, rho = -0.935171 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -1.223992, rho = -0.906747 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -1.734262, rho = -0.865860 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -2.440051, rho = -0.806800 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -3.396911, rho = -0.722092 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -4.652514, rho = -0.600243 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 59% (59/100) (classification) Accuracy = 53.1% (531/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -6.208702, rho = -0.424970 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 86% (86/100) (classification) Accuracy = 81.8% (818/1000) (classification) * optimization finished, #iter = 43 nu = 0.843142 obj = -7.959802, rho = -0.241185 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 96% (96/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 46 nu = 0.760822 obj = -9.945370, rho = -0.175746 nSV = 78, nBSV = 74 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.662261 obj = -12.331962, rho = -0.186485 nSV = 68, nBSV = 65 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 50 nu = 0.578934 obj = -15.040824, rho = -0.140884 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 37 nu = 0.492187 obj = -18.263662, rho = -0.153816 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 41 nu = 0.419385 obj = -22.200018, rho = -0.186541 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 50 nu = 0.349132 obj = -26.902704, rho = -0.195710 nSV = 38, nBSV = 33 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 78 nu = 0.295204 obj = -32.624194, rho = -0.132642 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 76 nu = 0.249067 obj = -39.420916, rho = -0.167504 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 64 nu = 0.209363 obj = -47.969742, rho = -0.081901 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 97 nu = 0.172424 obj = -58.939824, rho = -0.085818 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.150770 obj = -72.663775, rho = -0.127486 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 77 nu = 0.127382 obj = -89.191586, rho = -0.240360 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -0.935125, rho = 0.886931 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.329443, rho = 0.837356 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.879875, rho = 0.766045 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -2.636940, rho = 0.663468 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -3.654129, rho = 0.515915 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -4.968720, rho = 0.303668 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 68% (68/100) (classification) Accuracy = 64.9% (649/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -6.552249, rho = -0.001639 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 93% (93/100) (classification) Accuracy = 92.6% (926/1000) (classification) * optimization finished, #iter = 56 nu = 0.882141 obj = -8.362113, rho = -0.056182 nSV = 90, nBSV = 86 Total nSV = 90 Accuracy = 97% (97/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 41 nu = 0.792308 obj = -10.589484, rho = -0.152672 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 45 nu = 0.716070 obj = -13.182631, rho = -0.209286 nSV = 73, nBSV = 70 Total nSV = 73 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 48 nu = 0.620000 obj = -16.093579, rho = -0.160487 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.512601 obj = -19.670668, rho = -0.133387 nSV = 57, nBSV = 50 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 60 nu = 0.436143 obj = -24.299694, rho = -0.158266 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 53 nu = 0.382206 obj = -30.018622, rho = -0.007731 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 96 nu = 0.328551 obj = -36.591474, rho = 0.068908 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 45 nu = 0.285697 obj = -44.514897, rho = -0.053096 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 56 nu = 0.240982 obj = -52.789813, rho = -0.039565 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 199 nu = 0.201745 obj = -61.512976, rho = 0.026439 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 163 nu = 0.164046 obj = -71.337804, rho = -0.014547 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 140 nu = 0.131905 obj = -82.561475, rho = 0.002107 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 43 nu = 0.820000 obj = -0.803547, rho = -0.944910 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 43 nu = 0.820000 obj = -1.145486, rho = -0.920755 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 43 nu = 0.820000 obj = -1.626254, rho = -0.886010 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -2.294865, rho = -0.835540 nSV = 83, nBSV = 79 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -3.209129, rho = -0.763433 nSV = 83, nBSV = 79 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 46 nu = 0.820000 obj = -4.425980, rho = -0.660214 nSV = 83, nBSV = 79 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 48 nu = 0.820000 obj = -5.973020, rho = -0.511117 nSV = 83, nBSV = 79 Total nSV = 83 Accuracy = 75% (75/100) (classification) Accuracy = 66.5% (665/1000) (classification) * optimization finished, #iter = 49 nu = 0.820000 obj = -7.777616, rho = -0.296504 nSV = 83, nBSV = 79 Total nSV = 83 Accuracy = 94% (94/100) (classification) Accuracy = 92.7% (927/1000) (classification) * optimization finished, #iter = 43 nu = 0.745406 obj = -9.775197, rho = -0.191642 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 44 nu = 0.663444 obj = -12.031938, rho = -0.119149 nSV = 68, nBSV = 65 Total nSV = 68 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 42 nu = 0.571643 obj = -14.628379, rho = -0.146550 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 40 nu = 0.470424 obj = -17.727718, rho = -0.163815 nSV = 52, nBSV = 45 Total nSV = 52 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 47 nu = 0.399107 obj = -21.672486, rho = -0.144005 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 58 nu = 0.328989 obj = -26.738349, rho = -0.146574 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 28 nu = 0.291747 obj = -33.091422, rho = 0.010568 nSV = 31, nBSV = 27 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 40 nu = 0.249210 obj = -40.713383, rho = -0.107698 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 82 nu = 0.217087 obj = -49.816420, rho = -0.138558 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 189 nu = 0.177412 obj = -61.064121, rho = -0.172868 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 73 nu = 0.155079 obj = -76.160360, rho = -0.196684 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 58 nu = 0.138734 obj = -92.758189, rho = -0.073393 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 44 nu = 0.800000 obj = -0.784265, rho = 0.936584 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 44 nu = 0.800000 obj = -1.118201, rho = 0.908780 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 44 nu = 0.800000 obj = -1.587942, rho = 0.868784 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 44 nu = 0.800000 obj = -2.241687, rho = 0.811252 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 46 nu = 0.800000 obj = -3.136642, rho = 0.727709 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 46 nu = 0.800000 obj = -4.329998, rho = 0.608323 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 46 nu = 0.800000 obj = -5.852097, rho = 0.436593 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 73% (73/100) (classification) Accuracy = 65.3% (653/1000) (classification) * optimization finished, #iter = 44 nu = 0.800000 obj = -7.639152, rho = 0.189567 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 91% (91/100) (classification) Accuracy = 88.9% (889/1000) (classification) * optimization finished, #iter = 40 nu = 0.740000 obj = -9.563599, rho = 0.046061 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 96% (96/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 57 nu = 0.645332 obj = -11.749578, rho = -0.046212 nSV = 67, nBSV = 62 Total nSV = 67 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 30 nu = 0.552733 obj = -14.412661, rho = 0.016277 nSV = 56, nBSV = 54 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 55 nu = 0.476184 obj = -17.319139, rho = 0.040101 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 35 nu = 0.401463 obj = -20.812237, rho = -0.017883 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 144 nu = 0.338956 obj = -24.454496, rho = -0.091043 nSV = 38, nBSV = 30 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 53 nu = 0.274701 obj = -28.763274, rho = -0.037415 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.229575 obj = -33.633642, rho = -0.077799 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 52 nu = 0.188767 obj = -38.822043, rho = -0.147925 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.151720 obj = -44.053578, rho = -0.293724 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 156 nu = 0.117298 obj = -50.104925, rho = -0.304220 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 80 nu = 0.093785 obj = -57.747834, rho = -0.351528 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 46 nu = 0.840000 obj = -0.822367, rho = 0.923268 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 46 nu = 0.840000 obj = -1.171813, rho = 0.889624 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 46 nu = 0.840000 obj = -1.662584, rho = 0.841230 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 45 nu = 0.840000 obj = -2.343931, rho = 0.771617 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 45 nu = 0.840000 obj = -3.273111, rho = 0.671483 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 44 nu = 0.840000 obj = -4.504362, rho = 0.527444 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 58% (58/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 44 nu = 0.840000 obj = -6.057519, rho = 0.320252 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 79% (79/100) (classification) Accuracy = 74.9% (749/1000) (classification) * optimization finished, #iter = 45 nu = 0.834629 obj = -7.841313, rho = 0.041230 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 93% (93/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 49 nu = 0.744602 obj = -9.823340, rho = 0.022499 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 39 nu = 0.660000 obj = -12.189370, rho = 0.000963 nSV = 67, nBSV = 65 Total nSV = 67 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 42 nu = 0.566579 obj = -14.976772, rho = 0.026835 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 68 nu = 0.499067 obj = -18.186060, rho = -0.047243 nSV = 54, nBSV = 45 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 36 nu = 0.418175 obj = -21.856309, rho = -0.128327 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 48 nu = 0.351034 obj = -26.206387, rho = -0.167586 nSV = 38, nBSV = 30 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 41 nu = 0.286843 obj = -31.663269, rho = -0.192594 nSV = 31, nBSV = 28 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 68 nu = 0.249256 obj = -37.784994, rho = -0.305838 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 87 nu = 0.202876 obj = -44.648876, rho = -0.301638 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 151 nu = 0.162855 obj = -53.598804, rho = -0.265946 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 162 nu = 0.137340 obj = -65.173337, rho = -0.296787 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 262 nu = 0.115787 obj = -79.829947, rho = -0.358729 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -0.802720, rho = 0.942321 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -1.143775, rho = 0.917031 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -1.622713, rho = 0.880653 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -2.287534, rho = 0.827991 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -3.193960, rho = 0.752574 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -4.394592, rho = 0.644091 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 43 nu = 0.820000 obj = -5.908071, rho = 0.488042 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 78% (78/100) (classification) Accuracy = 63.4% (634/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -7.643227, rho = 0.263574 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 93% (93/100) (classification) Accuracy = 90.8% (908/1000) (classification) * optimization finished, #iter = 45 nu = 0.742633 obj = -9.486309, rho = 0.142759 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 98% (98/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 46 nu = 0.640365 obj = -11.597296, rho = 0.057091 nSV = 66, nBSV = 63 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 46 nu = 0.542717 obj = -14.098036, rho = 0.071579 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 60 nu = 0.460202 obj = -17.125048, rho = 0.020983 nSV = 49, nBSV = 41 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 36 nu = 0.382178 obj = -20.926874, rho = -0.004483 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.333305 obj = -25.358856, rho = 0.017705 nSV = 38, nBSV = 29 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 35 nu = 0.281641 obj = -30.540563, rho = 0.014672 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 32 nu = 0.240750 obj = -36.032910, rho = -0.083315 nSV = 27, nBSV = 22 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 71 nu = 0.194527 obj = -42.008148, rho = -0.087674 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.157983 obj = -49.395138, rho = -0.114728 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 94 nu = 0.133220 obj = -58.153629, rho = -0.111252 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 63 nu = 0.110459 obj = -66.740493, rho = -0.222796 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.951374, rho = 0.864805 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 52.3% (523/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.350449, rho = 0.805529 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 52.3% (523/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.905197, rho = 0.720263 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 52.3% (523/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.663234, rho = 0.597613 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 52.3% (523/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.670992, rho = 0.421186 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 55% (55/100) (classification) Accuracy = 54.9% (549/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.949607, rho = 0.167405 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 87% (87/100) (classification) Accuracy = 90.1% (901/1000) (classification) * optimization finished, #iter = 48 nu = 0.953635 obj = -6.453410, rho = -0.006661 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.889083 obj = -8.224250, rho = -0.025411 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 54 nu = 0.810798 obj = -10.168872, rho = 0.028718 nSV = 82, nBSV = 77 Total nSV = 82 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 35 nu = 0.680000 obj = -12.390934, rho = 0.028095 nSV = 69, nBSV = 67 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 38 nu = 0.589111 obj = -15.004146, rho = 0.058490 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 46 nu = 0.504096 obj = -18.070633, rho = 0.000187 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 48 nu = 0.420345 obj = -21.375243, rho = -0.020110 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 61 nu = 0.342279 obj = -25.377293, rho = -0.061145 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 53 nu = 0.286693 obj = -30.215608, rho = -0.161565 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 91 nu = 0.239309 obj = -35.533631, rho = -0.101145 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) .*..* optimization finished, #iter = 324 nu = 0.192505 obj = -41.472276, rho = -0.168686 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.155121 obj = -49.050300, rho = -0.237071 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) .*..* optimization finished, #iter = 370 nu = 0.127211 obj = -58.613605, rho = -0.270613 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.106967 obj = -70.130144, rho = -0.268500 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -0.861922, rho = -0.948436 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -1.228429, rho = -0.925828 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -1.743442, rho = -0.893307 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -2.459039, rho = -0.846527 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -3.436200, rho = -0.779237 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -4.733809, rho = -0.682444 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 59% (59/100) (classification) Accuracy = 52.2% (522/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -6.376911, rho = -0.543212 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 77% (77/100) (classification) Accuracy = 75.3% (753/1000) (classification) * optimization finished, #iter = 45 nu = 0.851901 obj = -8.298153, rho = -0.388201 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 92% (92/100) (classification) Accuracy = 92.1% (921/1000) (classification) * optimization finished, #iter = 43 nu = 0.801997 obj = -10.521278, rho = -0.242793 nSV = 82, nBSV = 79 Total nSV = 82 Accuracy = 95% (95/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 44 nu = 0.704134 obj = -12.986491, rho = -0.258062 nSV = 72, nBSV = 68 Total nSV = 72 Accuracy = 95% (95/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.604597 obj = -15.966590, rho = -0.218461 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 42 nu = 0.515624 obj = -19.628584, rho = -0.238891 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 28 nu = 0.452612 obj = -24.050117, rho = -0.294083 nSV = 46, nBSV = 43 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 38 nu = 0.388858 obj = -28.938667, rho = -0.310958 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 62 nu = 0.325520 obj = -34.580428, rho = -0.280019 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 80 nu = 0.262165 obj = -41.205308, rho = -0.280803 nSV = 32, nBSV = 22 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.220349 obj = -49.637128, rho = -0.243077 nSV = 27, nBSV = 17 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 96 nu = 0.188223 obj = -59.865390, rho = -0.126449 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) ..*.* optimization finished, #iter = 323 nu = 0.154661 obj = -71.217510, rho = -0.081548 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 83 nu = 0.127421 obj = -85.999716, rho = -0.153705 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 49 nu = 0.880000 obj = -0.860265, rho = 0.906970 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 56% (56/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 50 nu = 0.880000 obj = -1.225003, rho = 0.866440 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 56% (56/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 50 nu = 0.880000 obj = -1.736353, rho = 0.807881 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 56% (56/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 50 nu = 0.880000 obj = -2.444371, rho = 0.723646 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 56% (56/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 50 nu = 0.880000 obj = -3.405850, rho = 0.602479 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 56% (56/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 49 nu = 0.880000 obj = -4.671011, rho = 0.428186 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 60% (60/100) (classification) Accuracy = 53% (530/1000) (classification) * optimization finished, #iter = 48 nu = 0.880000 obj = -6.246975, rho = 0.177239 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 87% (87/100) (classification) Accuracy = 78.5% (785/1000) (classification) * optimization finished, #iter = 48 nu = 0.832345 obj = -8.064127, rho = 0.000168 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 93% (93/100) (classification) Accuracy = 90.7% (907/1000) (classification) * optimization finished, #iter = 44 nu = 0.766644 obj = -10.225904, rho = -0.130499 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 97% (97/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 41 nu = 0.686528 obj = -12.692719, rho = -0.105670 nSV = 70, nBSV = 68 Total nSV = 70 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 38 nu = 0.598490 obj = -15.521787, rho = -0.066494 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 43 nu = 0.520665 obj = -18.709737, rho = -0.031542 nSV = 56, nBSV = 49 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.443017 obj = -22.114269, rho = -0.026820 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 56 nu = 0.363333 obj = -25.969493, rho = -0.062808 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.291087 obj = -30.208277, rho = -0.104684 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 77 nu = 0.234067 obj = -35.639931, rho = -0.112994 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 80 nu = 0.197386 obj = -42.256690, rho = -0.007416 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 90 nu = 0.162262 obj = -49.386936, rho = 0.008214 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 288 nu = 0.129124 obj = -57.576164, rho = 0.054378 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) ...*..* optimization finished, #iter = 501 nu = 0.104520 obj = -67.496972, rho = 0.151450 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -0.889932, rho = -0.919935 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.261158, rho = -0.884831 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.774874, rho = -0.834334 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -2.471877, rho = -0.761698 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -3.387678, rho = -0.657215 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 61% (61/100) (classification) Accuracy = 55.6% (556/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -4.525402, rho = -0.506921 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 82% (82/100) (classification) Accuracy = 79.2% (792/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -5.849520, rho = -0.388115 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 93% (93/100) (classification) Accuracy = 91.3% (913/1000) (classification) * optimization finished, #iter = 47 nu = 0.783595 obj = -7.432647, rho = -0.325843 nSV = 80, nBSV = 76 Total nSV = 80 Accuracy = 96% (96/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 38 nu = 0.720000 obj = -9.292873, rho = -0.245436 nSV = 73, nBSV = 71 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 40 nu = 0.633950 obj = -11.316051, rho = -0.168051 nSV = 66, nBSV = 61 Total nSV = 66 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.543763 obj = -13.584654, rho = -0.173627 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 34 nu = 0.455187 obj = -16.194835, rho = -0.216606 nSV = 47, nBSV = 43 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 29 nu = 0.378744 obj = -19.116049, rho = -0.199938 nSV = 40, nBSV = 36 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 25 nu = 0.317083 obj = -22.472836, rho = -0.165332 nSV = 33, nBSV = 30 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 59 nu = 0.259455 obj = -25.850152, rho = -0.186720 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 36 nu = 0.207860 obj = -29.505582, rho = -0.118498 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.164938 obj = -33.530002, rho = -0.127047 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 41 nu = 0.133368 obj = -37.504122, rho = -0.109594 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *........* optimization finished, #iter = 890 nu = 0.104257 obj = -41.188951, rho = -0.163859 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 85 nu = 0.081363 obj = -44.544551, rho = -0.213012 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.969054, rho = 0.000152 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.374419, rho = 0.000219 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.936650, rho = 0.000315 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.702215, rho = 0.000453 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.714106, rho = 0.000652 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -4.984812, rho = 0.000938 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 52 nu = 0.993906 obj = -6.430629, rho = 0.004828 nSV = 100, nBSV = 97 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 47 nu = 0.902065 obj = -7.970460, rho = -0.066051 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -9.634363, rho = -0.053340 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.666730 obj = -11.392419, rho = -0.065518 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 48 nu = 0.551225 obj = -13.395867, rho = -0.049163 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 33 nu = 0.451245 obj = -15.741421, rho = -0.115098 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 62 nu = 0.363572 obj = -18.403788, rho = -0.122733 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 59 nu = 0.292297 obj = -21.742724, rho = -0.090763 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 38 nu = 0.242651 obj = -25.895806, rho = -0.109813 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 36 nu = 0.204292 obj = -30.536297, rho = -0.105389 nSV = 22, nBSV = 18 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 78 nu = 0.175460 obj = -35.012171, rho = -0.005465 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 182 nu = 0.134400 obj = -39.618372, rho = 0.037554 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 85 nu = 0.108002 obj = -44.816252, rho = 0.055540 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 141 nu = 0.082375 obj = -50.889757, rho = 0.021173 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -0.920951, rho = 0.907446 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -1.312729, rho = 0.866865 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -1.863436, rho = 0.808492 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -2.629025, rho = 0.724526 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -3.675295, rho = 0.603744 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -5.066519, rho = 0.430006 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 54% (54/100) (classification) Accuracy = 53.7% (537/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -6.832289, rho = 0.180092 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 76% (76/100) (classification) Accuracy = 83.6% (836/1000) (classification) * optimization finished, #iter = 47 nu = 0.930218 obj = -8.887336, rho = -0.149813 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 95% (95/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 51 nu = 0.842939 obj = -11.214175, rho = -0.239143 nSV = 87, nBSV = 83 Total nSV = 87 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 44 nu = 0.754670 obj = -13.956165, rho = -0.181606 nSV = 78, nBSV = 74 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 40 nu = 0.660217 obj = -17.027207, rho = -0.258235 nSV = 68, nBSV = 65 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 46 nu = 0.558484 obj = -20.447279, rho = -0.232518 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 67 nu = 0.472067 obj = -24.529130, rho = -0.280225 nSV = 50, nBSV = 43 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 45 nu = 0.389410 obj = -29.363315, rho = -0.292453 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 67 nu = 0.326951 obj = -35.159392, rho = -0.267000 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.268248 obj = -42.253896, rho = -0.277831 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 88 nu = 0.229899 obj = -50.829732, rho = -0.175564 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) ..* optimization finished, #iter = 276 nu = 0.186184 obj = -60.667610, rho = -0.150000 nSV = 24, nBSV = 13 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.151384 obj = -73.977796, rho = -0.135512 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 44 nu = 0.130796 obj = -91.907526, rho = -0.001387 nSV = 15, nBSV = 11 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -0.933603, rho = -0.905465 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.326293, rho = -0.864016 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.873358, rho = -0.804394 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.623454, rho = -0.718630 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.626225, rho = -0.595264 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.910983, rho = -0.417807 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 68% (68/100) (classification) Accuracy = 67.5% (675/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -6.432782, rho = -0.162545 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 93% (93/100) (classification) Accuracy = 93.4% (934/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -8.119359, rho = -0.084565 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 40 nu = 0.784018 obj = -10.102598, rho = -0.091571 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 52 nu = 0.685864 obj = -12.343398, rho = -0.053352 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 39 nu = 0.590673 obj = -14.922120, rho = -0.097803 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 42 nu = 0.490067 obj = -17.849772, rho = -0.143038 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 85 nu = 0.400335 obj = -21.583409, rho = -0.132929 nSV = 44, nBSV = 36 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 33 nu = 0.338667 obj = -26.432150, rho = -0.132019 nSV = 36, nBSV = 32 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 38 nu = 0.290151 obj = -32.154320, rho = -0.165846 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 31 nu = 0.247521 obj = -38.898311, rho = -0.187788 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 86 nu = 0.204855 obj = -46.901240, rho = -0.207864 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 61 nu = 0.167732 obj = -57.529689, rho = -0.232186 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 39 nu = 0.148300 obj = -70.900024, rho = -0.367330 nSV = 17, nBSV = 12 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 45 nu = 0.127973 obj = -85.821633, rho = -0.316667 nSV = 15, nBSV = 10 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -0.877936, rho = -0.936401 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 52.8% (528/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -1.248950, rho = -0.908515 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 52.8% (528/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -1.767759, rho = -0.868404 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 52.8% (528/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -2.483255, rho = -0.810706 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 52.8% (528/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -3.448762, rho = -0.727710 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 53.1% (531/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -4.705797, rho = -0.608324 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 77% (77/100) (classification) Accuracy = 71% (710/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -6.241271, rho = -0.436593 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 90% (90/100) (classification) Accuracy = 90.5% (905/1000) (classification) * optimization finished, #iter = 43 nu = 0.830412 obj = -8.027465, rho = -0.384348 nSV = 84, nBSV = 82 Total nSV = 84 Accuracy = 94% (94/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 44 nu = 0.757930 obj = -10.220271, rho = -0.339122 nSV = 78, nBSV = 74 Total nSV = 78 Accuracy = 96% (96/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 44 nu = 0.678272 obj = -12.783670, rho = -0.307145 nSV = 70, nBSV = 65 Total nSV = 70 Accuracy = 96% (96/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 42 nu = 0.600000 obj = -15.825719, rho = -0.280089 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 47 nu = 0.508335 obj = -19.379029, rho = -0.230560 nSV = 56, nBSV = 49 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 93 nu = 0.437972 obj = -23.666640, rho = -0.210298 nSV = 47, nBSV = 40 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 46 nu = 0.370762 obj = -28.962136, rho = -0.139546 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 87 nu = 0.320832 obj = -35.180106, rho = -0.160909 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 44 nu = 0.272894 obj = -42.152177, rho = -0.227664 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 67 nu = 0.227601 obj = -50.177184, rho = -0.285427 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.191809 obj = -59.706810, rho = -0.191673 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 58 nu = 0.158239 obj = -70.393289, rho = -0.163500 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 122 nu = 0.133401 obj = -82.064669, rho = -0.242640 nSV = 16, nBSV = 11 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -0.920016, rho = 0.902812 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 51 nu = 0.940000 obj = -1.310797, rho = 0.858949 nSV = 96, nBSV = 93 Total nSV = 96 Accuracy = 53% (53/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 51 nu = 0.940000 obj = -1.859440, rho = 0.797105 nSV = 96, nBSV = 93 Total nSV = 96 Accuracy = 53% (53/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -2.620756, rho = 0.708146 nSV = 96, nBSV = 93 Total nSV = 96 Accuracy = 53% (53/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -3.658185, rho = 0.580182 nSV = 96, nBSV = 93 Total nSV = 96 Accuracy = 53% (53/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -5.031117, rho = 0.396146 nSV = 96, nBSV = 93 Total nSV = 96 Accuracy = 57% (57/100) (classification) Accuracy = 54.9% (549/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -6.759039, rho = 0.131387 nSV = 96, nBSV = 93 Total nSV = 96 Accuracy = 81% (81/100) (classification) Accuracy = 82% (820/1000) (classification) * optimization finished, #iter = 46 nu = 0.919500 obj = -8.752735, rho = -0.096355 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 94% (94/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 49 nu = 0.843681 obj = -11.045090, rho = -0.117440 nSV = 87, nBSV = 83 Total nSV = 87 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 45 nu = 0.747643 obj = -13.615692, rho = -0.144828 nSV = 77, nBSV = 74 Total nSV = 77 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 61 nu = 0.646056 obj = -16.479762, rho = -0.171331 nSV = 68, nBSV = 61 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 58 nu = 0.544010 obj = -19.837927, rho = -0.166615 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 60 nu = 0.456278 obj = -23.690906, rho = -0.187802 nSV = 49, nBSV = 42 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 70 nu = 0.380044 obj = -28.204089, rho = -0.202694 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 73 nu = 0.316981 obj = -33.425888, rho = -0.248151 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 62 nu = 0.266681 obj = -39.312772, rho = -0.391890 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 261 nu = 0.214733 obj = -46.039658, rho = -0.386498 nSV = 26, nBSV = 15 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*..* optimization finished, #iter = 312 nu = 0.169525 obj = -54.566052, rho = -0.391653 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.138885 obj = -65.885240, rho = -0.415070 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 85 nu = 0.114100 obj = -80.808793, rho = -0.382323 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -0.934161, rho = 0.872176 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 46.1% (461/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.327448, rho = 0.816131 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 46.1% (461/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.875749, rho = 0.735514 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 46.1% (461/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.628402, rho = 0.619550 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 46.1% (461/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -3.636462, rho = 0.452742 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 47% (470/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -4.932165, rho = 0.212797 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 79% (79/100) (classification) Accuracy = 73.6% (736/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -6.476611, rho = -0.132352 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 96% (96/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 47 nu = 0.881451 obj = -8.248906, rho = -0.217165 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 94% (94/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 43 nu = 0.793832 obj = -10.374541, rho = -0.129289 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 60 nu = 0.697135 obj = -12.798489, rho = -0.044472 nSV = 73, nBSV = 68 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 65 nu = 0.599346 obj = -15.671604, rho = -0.002876 nSV = 63, nBSV = 56 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.506914 obj = -19.186611, rho = 0.049625 nSV = 53, nBSV = 50 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 39 nu = 0.437651 obj = -23.354396, rho = 0.006161 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 66 nu = 0.364125 obj = -28.442576, rho = 0.010082 nSV = 41, nBSV = 33 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.304562 obj = -34.855085, rho = 0.037866 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 97 nu = 0.266835 obj = -42.778178, rho = 0.203319 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.230291 obj = -51.253497, rho = 0.410937 nSV = 28, nBSV = 18 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 78 nu = 0.195460 obj = -61.456777, rho = 0.473988 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) *...* optimization finished, #iter = 310 nu = 0.158070 obj = -72.695576, rho = 0.504926 nSV = 21, nBSV = 10 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 196 nu = 0.131509 obj = -87.376895, rho = 0.476446 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -0.915145, rho = -0.931369 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.300715, rho = -0.901278 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.838577, rho = -0.857993 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.577589, rho = -0.795730 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.568866, rho = -0.706168 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -4.846302, rho = -0.577338 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 77% (77/100) (classification) Accuracy = 67.5% (675/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -6.376630, rho = -0.392021 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 94% (94/100) (classification) Accuracy = 91% (910/1000) (classification) * optimization finished, #iter = 48 nu = 0.862534 obj = -8.118026, rho = -0.317065 nSV = 89, nBSV = 85 Total nSV = 89 Accuracy = 96% (96/100) (classification) Accuracy = 94.6% (946/1000) (classification) * optimization finished, #iter = 41 nu = 0.780000 obj = -10.209113, rho = -0.285168 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 97% (97/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 42 nu = 0.695419 obj = -12.513693, rho = -0.197549 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 44 nu = 0.590434 obj = -15.162897, rho = -0.160149 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 49 nu = 0.503797 obj = -18.300343, rho = -0.129052 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 41 nu = 0.423359 obj = -21.852285, rho = -0.095360 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 33 nu = 0.353921 obj = -25.959141, rho = -0.154360 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.296495 obj = -30.638904, rho = -0.199931 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) *..* optimization finished, #iter = 211 nu = 0.235938 obj = -35.966108, rho = -0.179403 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 67 nu = 0.192425 obj = -42.898901, rho = -0.175495 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 92 nu = 0.167770 obj = -50.290517, rho = -0.360235 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 117 nu = 0.131721 obj = -58.165794, rho = -0.417692 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.104904 obj = -68.603425, rho = -0.444271 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.969516, rho = -0.029378 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 95% (95/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.375375, rho = -0.042258 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 95% (95/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.938627, rho = -0.060786 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 95% (95/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.706307, rho = -0.087438 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 95% (95/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.722573, rho = -0.125776 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 95% (95/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -5.002332, rho = -0.180922 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 95% (95/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -6.507846, rho = -0.162873 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 95% (95/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 45 nu = 0.894554 obj = -8.334664, rho = -0.155540 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 97% (97/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 42 nu = 0.798936 obj = -10.459949, rho = -0.196093 nSV = 81, nBSV = 77 Total nSV = 81 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 48 nu = 0.701156 obj = -12.929994, rho = -0.181205 nSV = 74, nBSV = 69 Total nSV = 74 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 40 nu = 0.607290 obj = -15.832262, rho = -0.177816 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 66 nu = 0.504895 obj = -19.453111, rho = -0.155035 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.431811 obj = -24.093694, rho = -0.169775 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 44 nu = 0.372667 obj = -29.883715, rho = -0.171939 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 56 nu = 0.328599 obj = -36.998365, rho = -0.145283 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 59 nu = 0.276327 obj = -45.536592, rho = -0.062682 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 97% (97/100) (classification) Accuracy = 99.3% (993/1000) (classification) * optimization finished, #iter = 79 nu = 0.233890 obj = -56.798432, rho = -0.107420 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 75 nu = 0.206233 obj = -70.636630, rho = -0.098296 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.174508 obj = -88.282195, rho = -0.126624 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 64 nu = 0.155766 obj = -110.425918, rho = -0.175543 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -0.878965, rho = 0.892298 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -1.251083, rho = 0.844437 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -1.772171, rho = 0.776231 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -2.492383, rho = 0.678119 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -3.467650, rho = 0.536991 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -4.744880, rho = 0.333985 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 61% (61/100) (classification) Accuracy = 62% (620/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -6.322137, rho = 0.041970 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 89% (89/100) (classification) Accuracy = 86.1% (861/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -8.106176, rho = -0.167954 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 96% (96/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 40 nu = 0.780000 obj = -10.159538, rho = -0.225622 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 44 nu = 0.687257 obj = -12.533301, rho = -0.195782 nSV = 71, nBSV = 66 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 0.587712 obj = -15.232983, rho = -0.107462 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 37 nu = 0.504642 obj = -18.371217, rho = -0.137111 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 78 nu = 0.421828 obj = -22.044505, rho = -0.092609 nSV = 46, nBSV = 38 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.343625 obj = -26.594372, rho = -0.086792 nSV = 42, nBSV = 32 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 34 nu = 0.295902 obj = -32.273944, rho = -0.106787 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 59 nu = 0.246740 obj = -38.697280, rho = -0.090352 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 163 nu = 0.208038 obj = -46.408941, rho = -0.162529 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *..* optimization finished, #iter = 263 nu = 0.172599 obj = -55.641088, rho = -0.189282 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.144476 obj = -66.653323, rho = -0.200479 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 74 nu = 0.123110 obj = -79.190836, rho = -0.214668 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -0.859507, rho = -0.940220 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 51.8% (518/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -1.223433, rho = -0.914010 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 51.8% (518/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -1.733104, rho = -0.876307 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 51.8% (518/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -2.437648, rho = -0.822074 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 51.8% (518/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -3.391938, rho = -0.744062 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 51.8% (518/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -4.642224, rho = -0.631846 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 57% (57/100) (classification) Accuracy = 54.5% (545/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -6.187410, rho = -0.470429 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 81% (81/100) (classification) Accuracy = 83.6% (836/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -7.897005, rho = -0.266416 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 98% (98/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 42 nu = 0.778619 obj = -9.717567, rho = -0.158307 nSV = 78, nBSV = 76 Total nSV = 78 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 40 nu = 0.656196 obj = -11.782530, rho = -0.133614 nSV = 67, nBSV = 62 Total nSV = 67 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 36 nu = 0.558950 obj = -14.281678, rho = -0.103149 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.478058 obj = -17.120760, rho = -0.114793 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 97 nu = 0.397663 obj = -20.236499, rho = -0.105332 nSV = 45, nBSV = 36 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 52 nu = 0.333553 obj = -23.649217, rho = -0.152730 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 71 nu = 0.265022 obj = -27.306540, rho = -0.095365 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 66 nu = 0.217414 obj = -31.587034, rho = 0.012644 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 67 nu = 0.172853 obj = -36.509019, rho = 0.060451 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.142012 obj = -41.878182, rho = 0.000608 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 53 nu = 0.114142 obj = -47.363095, rho = -0.013372 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 86 nu = 0.091269 obj = -53.076195, rho = -0.115852 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -0.857777, rho = 0.894137 nSV = 90, nBSV = 86 Total nSV = 90 Accuracy = 56% (56/100) (classification) Accuracy = 45.4% (454/1000) (classification) * optimization finished, #iter = 48 nu = 0.880000 obj = -1.219856, rho = 0.847602 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 45.4% (454/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -1.725703, rho = 0.780783 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 45.4% (454/1000) (classification) * optimization finished, #iter = 48 nu = 0.880000 obj = -2.422336, rho = 0.685028 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 45.4% (454/1000) (classification) * optimization finished, #iter = 49 nu = 0.880000 obj = -3.360257, rho = 0.546030 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 45.4% (454/1000) (classification) * optimization finished, #iter = 49 nu = 0.880000 obj = -4.576673, rho = 0.346987 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 62% (62/100) (classification) Accuracy = 58.4% (584/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -6.051777, rho = 0.060278 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 92% (92/100) (classification) Accuracy = 87.6% (876/1000) (classification) * optimization finished, #iter = 48 nu = 0.846759 obj = -7.640123, rho = -0.152844 nSV = 87, nBSV = 82 Total nSV = 87 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 41 nu = 0.747941 obj = -9.372696, rho = -0.099891 nSV = 76, nBSV = 74 Total nSV = 76 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 44 nu = 0.635163 obj = -11.321233, rho = -0.096789 nSV = 66, nBSV = 60 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 45 nu = 0.526968 obj = -13.721624, rho = -0.116214 nSV = 57, nBSV = 51 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 36 nu = 0.447475 obj = -16.673041, rho = -0.154369 nSV = 47, nBSV = 43 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 64 nu = 0.380299 obj = -20.154771, rho = -0.090708 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 40 nu = 0.320932 obj = -24.358185, rho = -0.053842 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.270075 obj = -29.259037, rho = 0.088576 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 61 nu = 0.224924 obj = -35.130326, rho = 0.088783 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 89 nu = 0.184999 obj = -42.431976, rho = 0.002511 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 98 nu = 0.150249 obj = -52.182107, rho = -0.028572 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 49 nu = 0.137098 obj = -64.138693, rho = -0.119891 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 92 nu = 0.115852 obj = -76.438644, rho = -0.198518 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -0.892661, rho = -0.933869 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.266806, rho = -0.904874 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.786559, rho = -0.863166 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -2.496059, rho = -0.803004 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -3.437712, rho = -0.716632 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 56% (56/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -4.628931, rho = -0.592389 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 79% (79/100) (classification) Accuracy = 68.4% (684/1000) (classification) * optimization finished, #iter = 46 nu = 0.898944 obj = -6.011156, rho = -0.453505 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 91% (91/100) (classification) Accuracy = 87.5% (875/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -7.644670, rho = -0.368009 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 96% (96/100) (classification) Accuracy = 94.7% (947/1000) (classification) * optimization finished, #iter = 38 nu = 0.723631 obj = -9.530041, rho = -0.342439 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 96% (96/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 39 nu = 0.633745 obj = -11.829508, rho = -0.355524 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 96% (96/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 44 nu = 0.550878 obj = -14.668046, rho = -0.331727 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 42 nu = 0.481593 obj = -18.008700, rho = -0.330606 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 96% (96/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 75 nu = 0.410777 obj = -21.906995, rho = -0.259840 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 41 nu = 0.344720 obj = -26.583339, rho = -0.238346 nSV = 36, nBSV = 32 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 45 nu = 0.296352 obj = -32.183976, rho = -0.110367 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 60 nu = 0.243478 obj = -39.026946, rho = -0.180945 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 79 nu = 0.202563 obj = -47.663429, rho = -0.181289 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 44 nu = 0.171214 obj = -59.167879, rho = -0.234015 nSV = 20, nBSV = 15 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 33 nu = 0.148506 obj = -73.641862, rho = -0.150224 nSV = 18, nBSV = 13 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 44 nu = 0.129468 obj = -91.182677, rho = -0.263227 nSV = 16, nBSV = 11 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.954560, rho = -0.919160 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.357043, rho = -0.883716 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.918839, rho = -0.832732 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.691462, rho = -0.759393 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.729401, rho = -0.653899 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -5.070463, rho = -0.502151 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 78% (78/100) (classification) Accuracy = 76.9% (769/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -6.685087, rho = -0.292433 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 90% (90/100) (classification) Accuracy = 93.1% (931/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -8.555245, rho = -0.324625 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 97% (97/100) (classification) Accuracy = 94.3% (943/1000) (classification) * optimization finished, #iter = 44 nu = 0.800000 obj = -10.867532, rho = -0.309138 nSV = 80, nBSV = 79 Total nSV = 80 Accuracy = 98% (98/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 48 nu = 0.718516 obj = -13.687599, rho = -0.187845 nSV = 73, nBSV = 70 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 41 nu = 0.640000 obj = -16.993559, rho = -0.211581 nSV = 65, nBSV = 62 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 50 nu = 0.554896 obj = -20.739622, rho = -0.238795 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 49 nu = 0.470090 obj = -25.149513, rho = -0.220855 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 56 nu = 0.399450 obj = -30.407432, rho = -0.197277 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 70 nu = 0.348251 obj = -36.360932, rho = -0.320159 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 43 nu = 0.283588 obj = -42.694665, rho = -0.359656 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 120 nu = 0.230637 obj = -50.059641, rho = -0.395496 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 145 nu = 0.188521 obj = -59.084204, rho = -0.345578 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 197 nu = 0.151663 obj = -70.333305, rho = -0.364054 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 91 nu = 0.122053 obj = -85.661753, rho = -0.364068 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -0.894073, rho = 0.874876 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.269728, rho = 0.820016 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.792606, rho = 0.741102 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -2.508566, rho = 0.627587 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -3.463592, rho = 0.464303 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -4.682478, rho = 0.229427 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 77% (77/100) (classification) Accuracy = 70% (700/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -6.115339, rho = -0.108431 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 95% (95/100) (classification) Accuracy = 92.4% (924/1000) (classification) * optimization finished, #iter = 46 nu = 0.849071 obj = -7.693300, rho = -0.128081 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 98% (98/100) (classification) Accuracy = 94.2% (942/1000) (classification) * optimization finished, #iter = 49 nu = 0.758036 obj = -9.454752, rho = -0.192852 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 98% (98/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 41 nu = 0.642008 obj = -11.397182, rho = -0.150467 nSV = 67, nBSV = 62 Total nSV = 67 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 42 nu = 0.553494 obj = -13.611117, rho = -0.147275 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 84 nu = 0.454335 obj = -16.064710, rho = -0.177128 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 32 nu = 0.373786 obj = -19.056229, rho = -0.191733 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 73 nu = 0.307483 obj = -22.469747, rho = -0.182214 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 79 nu = 0.250036 obj = -26.747245, rho = -0.250654 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.203392 obj = -32.000177, rho = -0.224318 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 42 nu = 0.174124 obj = -38.580119, rho = -0.292820 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 63 nu = 0.147962 obj = -45.721756, rho = -0.294870 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.119961 obj = -53.624244, rho = -0.330269 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.098977 obj = -63.080951, rho = -0.296656 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.949899, rho = 0.873142 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.347397, rho = 0.817522 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.898881, rho = 0.737514 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.650165, rho = 0.622427 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.643952, rho = 0.456880 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.893657, rho = 0.218749 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 82% (82/100) (classification) Accuracy = 79.5% (795/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -6.319251, rho = -0.118490 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 49 nu = 0.881140 obj = -7.855546, rho = -0.029586 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 96% (96/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 56 nu = 0.759419 obj = -9.625547, rho = -0.004648 nSV = 78, nBSV = 73 Total nSV = 78 Accuracy = 96% (96/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 39 nu = 0.655587 obj = -11.717044, rho = -0.070347 nSV = 67, nBSV = 63 Total nSV = 67 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 50 nu = 0.554116 obj = -14.158150, rho = -0.083631 nSV = 59, nBSV = 52 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 50 nu = 0.466320 obj = -17.022096, rho = -0.119075 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 55 nu = 0.389260 obj = -20.400627, rho = -0.092814 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 97 nu = 0.328721 obj = -24.455872, rho = -0.183346 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 50 nu = 0.276339 obj = -29.095402, rho = -0.162373 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 92 nu = 0.231189 obj = -34.045577, rho = -0.365384 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 72 nu = 0.190055 obj = -39.249661, rho = -0.505761 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 167 nu = 0.155686 obj = -43.857060, rho = -0.519973 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.122084 obj = -48.427375, rho = -0.468681 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*..* optimization finished, #iter = 309 nu = 0.093922 obj = -51.797410, rho = -0.480843 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -0.916975, rho = 0.906848 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 46.4% (464/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -1.304500, rho = 0.866005 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 46.4% (464/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -1.846410, rho = 0.807255 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 46.4% (464/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -2.593796, rho = 0.722746 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 46.4% (464/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -3.602401, rho = 0.601184 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 46.4% (464/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -4.915691, rho = 0.426324 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 60% (60/100) (classification) Accuracy = 55.1% (551/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -6.520206, rho = 0.174795 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 95% (95/100) (classification) Accuracy = 93% (930/1000) (classification) * optimization finished, #iter = 48 nu = 0.900576 obj = -8.297078, rho = 0.022702 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 56 nu = 0.811112 obj = -10.153295, rho = 0.043431 nSV = 85, nBSV = 78 Total nSV = 85 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 45 nu = 0.686629 obj = -12.299604, rho = 0.074983 nSV = 71, nBSV = 68 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 49 nu = 0.584754 obj = -14.827352, rho = 0.028094 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 40 nu = 0.492976 obj = -17.683135, rho = -0.047764 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 64 nu = 0.399180 obj = -21.173980, rho = -0.041019 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 60 nu = 0.337166 obj = -25.651110, rho = -0.084631 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 47 nu = 0.283402 obj = -31.033094, rho = -0.158610 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 48 nu = 0.240777 obj = -37.446838, rho = -0.161121 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 61 nu = 0.195208 obj = -45.164236, rho = -0.158465 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.163075 obj = -55.142438, rho = -0.168468 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.136478 obj = -68.484676, rho = -0.238840 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.118130 obj = -86.024262, rho = -0.201497 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -0.876921, rho = -0.934283 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -1.246851, rho = -0.905470 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -1.763416, rho = -0.864023 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -2.474267, rho = -0.804404 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -3.430166, rho = -0.718645 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -4.667320, rho = -0.595285 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 66% (66/100) (classification) Accuracy = 64.7% (647/1000) (classification) * optimization finished, #iter = 52 nu = 0.892874 obj = -6.162541, rho = -0.431028 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 89% (89/100) (classification) Accuracy = 86.9% (869/1000) (classification) * optimization finished, #iter = 48 nu = 0.848127 obj = -7.847461, rho = -0.250961 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 99% (99/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 51 nu = 0.760627 obj = -9.747399, rho = -0.148075 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 42 nu = 0.670885 obj = -11.846024, rho = -0.015565 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 59 nu = 0.567893 obj = -14.156715, rho = -0.013840 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 43 nu = 0.469304 obj = -16.930070, rho = -0.017298 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 53 nu = 0.387308 obj = -20.207020, rho = -0.013808 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 55 nu = 0.317820 obj = -24.401500, rho = -0.032113 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 36 nu = 0.275600 obj = -29.258950, rho = -0.033132 nSV = 30, nBSV = 26 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 56 nu = 0.226871 obj = -34.750246, rho = -0.043971 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 85 nu = 0.187625 obj = -41.402524, rho = -0.109194 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 72 nu = 0.154262 obj = -49.519714, rho = 0.070384 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.130830 obj = -59.098780, rho = 0.064317 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 89 nu = 0.109675 obj = -69.249233, rho = 0.077939 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -0.924546, rho = 0.811368 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.307553, rho = 0.728663 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.834583, rho = 0.609695 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.543224, rho = 0.438566 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.460218, rho = 0.192405 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 71% (71/100) (classification) Accuracy = 65% (650/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.567490, rho = -0.161685 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 90% (90/100) (classification) Accuracy = 88.8% (888/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -5.837503, rho = -0.267747 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 95% (95/100) (classification) Accuracy = 93.3% (933/1000) (classification) * optimization finished, #iter = 45 nu = 0.795442 obj = -7.398290, rho = -0.238223 nSV = 81, nBSV = 77 Total nSV = 81 Accuracy = 99% (99/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 43 nu = 0.716812 obj = -9.221194, rho = -0.279716 nSV = 73, nBSV = 69 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 36 nu = 0.627198 obj = -11.266385, rho = -0.256670 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 41 nu = 0.534768 obj = -13.611006, rho = -0.226652 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 38 nu = 0.453366 obj = -16.240522, rho = -0.183459 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 87 nu = 0.370649 obj = -19.302004, rho = -0.225149 nSV = 41, nBSV = 33 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.313227 obj = -23.063362, rho = -0.149998 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 38 nu = 0.254816 obj = -27.483132, rho = -0.170636 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 83 nu = 0.217675 obj = -32.488403, rho = -0.221724 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 145 nu = 0.182656 obj = -37.555817, rho = -0.222172 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 52 nu = 0.150037 obj = -42.663180, rho = -0.198470 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 267 nu = 0.119933 obj = -47.313853, rho = -0.222564 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.095026 obj = -50.569618, rho = -0.226895 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 38 nu = 0.760000 obj = -0.746067, rho = -0.960213 nSV = 76, nBSV = 76 Total nSV = 76 Accuracy = 62% (62/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 38 nu = 0.760000 obj = -1.064392, rho = -0.942768 nSV = 76, nBSV = 76 Total nSV = 76 Accuracy = 62% (62/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 38 nu = 0.760000 obj = -1.512892, rho = -0.917675 nSV = 76, nBSV = 76 Total nSV = 76 Accuracy = 62% (62/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 38 nu = 0.760000 obj = -2.138597, rho = -0.881580 nSV = 76, nBSV = 76 Total nSV = 76 Accuracy = 62% (62/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 38 nu = 0.760000 obj = -2.998420, rho = -0.829659 nSV = 76, nBSV = 76 Total nSV = 76 Accuracy = 62% (62/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 38 nu = 0.760000 obj = -4.152003, rho = -0.754973 nSV = 76, nBSV = 76 Total nSV = 76 Accuracy = 62% (62/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 38 nu = 0.760000 obj = -5.639166, rho = -0.647541 nSV = 76, nBSV = 76 Total nSV = 76 Accuracy = 67% (67/100) (classification) Accuracy = 55.2% (552/1000) (classification) * optimization finished, #iter = 38 nu = 0.760000 obj = -7.422049, rho = -0.493005 nSV = 76, nBSV = 76 Total nSV = 76 Accuracy = 91% (91/100) (classification) Accuracy = 84.3% (843/1000) (classification) * optimization finished, #iter = 43 nu = 0.719636 obj = -9.324872, rho = -0.339514 nSV = 73, nBSV = 70 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 40 nu = 0.632063 obj = -11.489132, rho = -0.267388 nSV = 65, nBSV = 60 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 39 nu = 0.537263 obj = -14.057131, rho = -0.195189 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 33 nu = 0.461900 obj = -17.095530, rho = -0.258972 nSV = 48, nBSV = 45 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 30 nu = 0.400076 obj = -20.452706, rho = -0.440064 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 69 nu = 0.333469 obj = -24.120091, rho = -0.469749 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 136 nu = 0.274080 obj = -28.161486, rho = -0.513109 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *..* optimization finished, #iter = 200 nu = 0.223275 obj = -32.689881, rho = -0.549908 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 170 nu = 0.176697 obj = -37.938808, rho = -0.544589 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.145490 obj = -44.548370, rho = -0.540402 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 221 nu = 0.123101 obj = -50.516487, rho = -0.493774 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.096944 obj = -56.473418, rho = -0.513572 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 47 nu = 0.860000 obj = -0.840182, rho = 0.911188 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 47 nu = 0.860000 obj = -1.196061, rho = 0.872248 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -1.694611, rho = 0.816235 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -2.384101, rho = 0.735663 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 47 nu = 0.860000 obj = -3.318691, rho = 0.620000 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 47 nu = 0.860000 obj = -4.544671, rho = 0.453389 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 58% (58/100) (classification) Accuracy = 53.1% (531/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -6.063241, rho = 0.213728 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 81% (81/100) (classification) Accuracy = 79.9% (799/1000) (classification) * optimization finished, #iter = 47 nu = 0.846355 obj = -7.745054, rho = -0.073870 nSV = 87, nBSV = 83 Total nSV = 87 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 39 nu = 0.753212 obj = -9.555329, rho = -0.034258 nSV = 76, nBSV = 74 Total nSV = 76 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 47 nu = 0.649647 obj = -11.595831, rho = -0.055199 nSV = 67, nBSV = 63 Total nSV = 67 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 42 nu = 0.555632 obj = -13.959863, rho = -0.053841 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 36 nu = 0.462612 obj = -16.751580, rho = -0.096646 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 41 nu = 0.383021 obj = -20.131554, rho = -0.153375 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.322064 obj = -24.125349, rho = -0.139358 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 38 nu = 0.268624 obj = -28.895325, rho = -0.123549 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.230288 obj = -34.234832, rho = -0.132395 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 61 nu = 0.189039 obj = -39.940737, rho = -0.059139 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *..* optimization finished, #iter = 222 nu = 0.149620 obj = -46.688830, rho = -0.024630 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 178 nu = 0.122738 obj = -55.350834, rho = -0.127040 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 84 nu = 0.101844 obj = -65.257992, rho = -0.225265 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 49 nu = 0.900000 obj = -0.880321, rho = 0.927847 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 49 nu = 0.900000 obj = -1.253886, rho = 0.896211 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 50 nu = 0.900000 obj = -1.777971, rho = 0.850695 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -2.504385, rho = 0.785217 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -3.492484, rho = 0.691045 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -4.796265, rho = 0.555584 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 56% (56/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -6.428461, rho = 0.360730 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 76% (76/100) (classification) Accuracy = 74.2% (742/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -8.283101, rho = 0.122131 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 98% (98/100) (classification) Accuracy = 93.2% (932/1000) (classification) * optimization finished, #iter = 44 nu = 0.809545 obj = -10.203961, rho = -0.041477 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 40 nu = 0.695528 obj = -12.430692, rho = 0.014160 nSV = 71, nBSV = 68 Total nSV = 71 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 46 nu = 0.589894 obj = -14.988993, rho = 0.014217 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 40 nu = 0.496143 obj = -17.979484, rho = 0.018501 nSV = 54, nBSV = 47 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 27 nu = 0.424591 obj = -21.371396, rho = -0.088446 nSV = 45, nBSV = 41 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 32 nu = 0.345661 obj = -25.165308, rho = -0.120024 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 62 nu = 0.291606 obj = -29.294833, rho = -0.139951 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 66 nu = 0.236762 obj = -33.445850, rho = -0.116292 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 60 nu = 0.184796 obj = -38.137498, rho = -0.067144 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 67 nu = 0.145563 obj = -43.806560, rho = -0.066623 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 98 nu = 0.116988 obj = -50.368335, rho = -0.014313 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 180 nu = 0.097200 obj = -57.134888, rho = -0.086703 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification)
In [ ]:
import numpy as np
import numpy.matlib as matlib
from libsvm.svmutil import *
import matplotlib.pyplot as plt
def data(N,sigma):
w = np.ones(10)/np.sqrt(10)
w1 = [1., 1., 1., 1., 1., -1., -1., -1., -1., -1.]/np.sqrt(10)
w2 = [-1., -1., 0, 1., 1., -1., -1., 0, -1., -1.]/np.sqrt(8)
x = np.zeros((4,10))
x[1,:] = x[0,:] + sigma*w1
x[2,:] = x[0,:] + sigma*w2
x[3,:] = x[2,:] + sigma*w1
X1 = x + sigma*matlib.repmat(w,4,1)/2
X2 = x - sigma*matlib.repmat(w,4,1)/2
X1 = matlib.repmat(X1,2*N,1)
X2 = matlib.repmat(X2,2*N,1)
X = np.concatenate((X1, X2), axis=0)
Y = np.concatenate((np.ones(4*2*N), -np.ones(4*2*N)),axis=0)
Z = np.random.permutation(16*N)
Z = Z[:N]
X = X[Z,:]
X = X + 0.2*sigma*np.random.randn(N,10)
Y = Y[Z]
return X, Y
# Task 2a: Generating Parameter Values
lambda_values = np.logspace(-2, 1, 20) # Logarithmically spaced values between 0.01 and 10
# Initialize arrays to store errors
training_errors = []
test_errors = []
sigma = 0.5
# Task 2b-d: Training, Testing, and Repeating the Experiment
# num_iterations = 100
for i in range(num_iterations):
# Generate data
X_train, y_train = data(100,sigma)
X_test, y_test = data(1000, sigma)
for lam in lambda_values:
# Train SVM
svm_problem_setup = svm_problem(y_train.tolist(), X_train.tolist())
param = svm_parameter(f'-t 0 -c {lam}')
model = svm_train(svm_problem_setup, param)
# Predict on training and test data
i, train_accuracy, i = svm_predict(y_train.tolist(), X_train.tolist(), model)
i, test_accuracy, i = svm_predict(y_test.tolist(), X_test.tolist(), model)
# Calculate errors
training_errors.append(100 - train_accuracy[0]) # Convert to error percentage
test_errors.append(100 - test_accuracy[0]) # Convert to error percentage
# Task 2e: Averaging Errors and Plotting
training_errors = np.array(training_errors).reshape(num_iterations, -1)
test_errors = np.array(test_errors).reshape(num_iterations, -1)
avg_training_error = np.mean(training_errors, axis=0)
avg_test_error = np.mean(test_errors, axis=0)
lambda_values_log = np.log10(lambda_values)
# Plotting
plt.figure(figsize=(10, 6))
plt.plot(lambda_values_log, avg_training_error, label='R_empirical (Average Training Error)')
plt.plot(lambda_values_log, avg_test_error, label='R_actual (Average Test Error)')
plt.plot(lambda_values_log, avg_test_error - avg_training_error, label='R_structural (Difference)')
plt.xlabel('log(λ)')
plt.ylabel('Error (%)')
plt.title('Risks vs. λ (0.01,10) @ σ = 1')
plt.legend()
plt.show()
* optimization finished, #iter = 49 nu = 0.960000 obj = -0.932719, rho = 0.857246 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.324464, rho = 0.794656 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.869574, rho = 0.704623 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -2.615625, rho = 0.575114 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.610025, rho = 0.388823 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 53% (53/100) (classification) Accuracy = 51.8% (518/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.877462, rho = 0.120853 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 75% (75/100) (classification) Accuracy = 77.5% (775/1000) (classification) * optimization finished, #iter = 49 nu = 0.951705 obj = -6.364395, rho = -0.213655 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 93% (93/100) (classification) Accuracy = 93.3% (933/1000) (classification) * optimization finished, #iter = 52 nu = 0.857639 obj = -8.088236, rho = -0.202594 nSV = 88, nBSV = 84 Total nSV = 88 Accuracy = 95% (95/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 41 nu = 0.774771 obj = -10.175957, rho = -0.204820 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 96% (96/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 49 nu = 0.690525 obj = -12.518759, rho = -0.110609 nSV = 72, nBSV = 67 Total nSV = 72 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 71 nu = 0.582858 obj = -15.208185, rho = -0.120108 nSV = 63, nBSV = 55 Total nSV = 63 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 34 nu = 0.499728 obj = -18.583098, rho = -0.212492 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 33 nu = 0.421808 obj = -22.535245, rho = -0.217710 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 39 nu = 0.359568 obj = -27.362129, rho = -0.117363 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 63 nu = 0.302488 obj = -32.647133, rho = -0.132588 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 183 nu = 0.248027 obj = -39.256698, rho = -0.138070 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.209878 obj = -47.638099, rho = -0.266255 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 151 nu = 0.177147 obj = -57.635583, rho = -0.220701 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 95 nu = 0.145467 obj = -69.792219, rho = -0.186210 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 95 nu = 0.120792 obj = -85.877231, rho = -0.205660 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -0.898093, rho = 0.904199 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -1.278046, rho = 0.862195 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.809817, rho = 0.801774 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -2.544179, rho = 0.714861 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -3.537281, rho = 0.590619 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -4.834953, rho = 0.411125 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 61% (61/100) (classification) Accuracy = 63.4% (634/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -6.430829, rho = 0.152933 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 88% (88/100) (classification) Accuracy = 92.1% (921/1000) (classification) * optimization finished, #iter = 48 nu = 0.879999 obj = -8.241725, rho = -0.014584 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 94% (94/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 47 nu = 0.790686 obj = -10.332252, rho = -0.027441 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 96% (96/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 52 nu = 0.684615 obj = -12.789375, rho = -0.037106 nSV = 71, nBSV = 66 Total nSV = 71 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.597582 obj = -15.716381, rho = -0.065441 nSV = 64, nBSV = 58 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 42 nu = 0.515083 obj = -19.172420, rho = -0.108715 nSV = 55, nBSV = 48 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 46 nu = 0.434847 obj = -23.386882, rho = -0.128425 nSV = 45, nBSV = 41 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 80 nu = 0.364242 obj = -28.551892, rho = -0.152445 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 64 nu = 0.312247 obj = -34.822180, rho = -0.168637 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 51 nu = 0.268101 obj = -42.386101, rho = -0.187802 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 80 nu = 0.224555 obj = -51.119587, rho = -0.297038 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 54 nu = 0.187032 obj = -62.492587, rho = -0.214722 nSV = 21, nBSV = 17 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 77 nu = 0.171410 obj = -74.631787, rho = -0.037012 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 141 nu = 0.136896 obj = -86.312663, rho = 0.003816 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.968730, rho = -0.021037 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 93% (93/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.373748, rho = -0.030261 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 93% (93/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.935262, rho = -0.043529 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 93% (93/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.699343, rho = -0.062614 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 93% (93/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.708163, rho = -0.090067 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 93% (93/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -4.972516, rho = -0.129557 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 93% (93/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -6.417230, rho = -0.177928 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 93% (93/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 49 nu = 0.889353 obj = -8.028078, rho = -0.206359 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 94% (94/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 42 nu = 0.770971 obj = -9.897946, rho = -0.200629 nSV = 80, nBSV = 76 Total nSV = 80 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 48 nu = 0.671983 obj = -12.057635, rho = -0.215367 nSV = 70, nBSV = 63 Total nSV = 70 Accuracy = 96% (96/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 53 nu = 0.569148 obj = -14.595200, rho = -0.176582 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 96% (96/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 45 nu = 0.471198 obj = -17.763894, rho = -0.184792 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 49 nu = 0.398132 obj = -21.754079, rho = -0.236250 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 39 nu = 0.337122 obj = -26.724016, rho = -0.237300 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 61 nu = 0.283802 obj = -33.029752, rho = -0.199041 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.250033 obj = -41.038689, rho = -0.215949 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 48 nu = 0.213063 obj = -50.304116, rho = -0.169703 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 85 nu = 0.179490 obj = -62.321105, rho = -0.110819 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 42 nu = 0.158709 obj = -77.770361, rho = -0.150115 nSV = 18, nBSV = 14 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 41 nu = 0.143762 obj = -95.069754, rho = -0.182772 nSV = 17, nBSV = 13 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.954146, rho = -0.921462 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.4% (494/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.356185, rho = -0.887027 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.4% (494/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.917066, rho = -0.837494 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.4% (494/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -2.687794, rho = -0.766460 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 49.4% (494/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -3.721809, rho = -0.664064 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -5.054755, rho = -0.516773 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 75% (75/100) (classification) Accuracy = 74.1% (741/1000) (classification) * optimization finished, #iter = 49 nu = 0.971652 obj = -6.653430, rho = -0.321095 nSV = 98, nBSV = 96 Total nSV = 98 Accuracy = 96% (96/100) (classification) Accuracy = 94.3% (943/1000) (classification) * optimization finished, #iter = 45 nu = 0.896721 obj = -8.533212, rho = -0.302460 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 97% (97/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 42 nu = 0.817301 obj = -10.753280, rho = -0.265572 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.724990 obj = -13.285716, rho = -0.224720 nSV = 75, nBSV = 69 Total nSV = 75 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 38 nu = 0.631425 obj = -16.208343, rho = -0.118691 nSV = 64, nBSV = 61 Total nSV = 64 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 39 nu = 0.533299 obj = -19.463031, rho = -0.122601 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 36 nu = 0.450345 obj = -23.234464, rho = -0.171379 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 46 nu = 0.368012 obj = -27.678326, rho = -0.155954 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 44 nu = 0.311845 obj = -33.084929, rho = -0.033818 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 38 nu = 0.256016 obj = -39.382509, rho = 0.014259 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.210588 obj = -46.745578, rho = -0.005772 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 53 nu = 0.172246 obj = -56.134598, rho = 0.095689 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *..* optimization finished, #iter = 206 nu = 0.145146 obj = -66.979935, rho = 0.104785 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.120472 obj = -80.508125, rho = 0.133241 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -0.898007, rho = -0.943066 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.277869, rho = -0.918100 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.809450, rho = -0.882196 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -2.543419, rho = -0.830545 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -3.535707, rho = -0.756247 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -4.831699, rho = -0.649357 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 69% (69/100) (classification) Accuracy = 65.9% (659/1000) (classification) * optimization finished, #iter = 52 nu = 0.919111 obj = -6.424108, rho = -0.496772 nSV = 93, nBSV = 90 Total nSV = 93 Accuracy = 87% (87/100) (classification) Accuracy = 88.8% (888/1000) (classification) * optimization finished, #iter = 58 nu = 0.858390 obj = -8.321093, rho = -0.378842 nSV = 88, nBSV = 84 Total nSV = 88 Accuracy = 94% (94/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 54 nu = 0.789322 obj = -10.590077, rho = -0.289538 nSV = 82, nBSV = 77 Total nSV = 82 Accuracy = 95% (95/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 48 nu = 0.704599 obj = -13.192672, rho = -0.213818 nSV = 72, nBSV = 70 Total nSV = 72 Accuracy = 96% (96/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 41 nu = 0.622247 obj = -16.241950, rho = -0.196377 nSV = 65, nBSV = 60 Total nSV = 65 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 57 nu = 0.534713 obj = -19.689547, rho = -0.197295 nSV = 56, nBSV = 50 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 88 nu = 0.451489 obj = -23.656464, rho = -0.217344 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 53 nu = 0.371788 obj = -28.547712, rho = -0.277060 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 64 nu = 0.309620 obj = -34.755049, rho = -0.282293 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 28 nu = 0.267490 obj = -42.317149, rho = -0.350585 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 38 nu = 0.224645 obj = -51.088307, rho = -0.466098 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 51 nu = 0.195205 obj = -61.174528, rho = -0.515886 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 40 nu = 0.165210 obj = -72.092677, rho = -0.570044 nSV = 18, nBSV = 14 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 90 nu = 0.135053 obj = -82.481774, rho = -0.671566 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 40 nu = 0.720000 obj = -0.709135, rho = 0.958393 nSV = 73, nBSV = 71 Total nSV = 73 Accuracy = 64% (64/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 40 nu = 0.720000 obj = -1.013202, rho = 0.940151 nSV = 73, nBSV = 71 Total nSV = 73 Accuracy = 64% (64/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 41 nu = 0.720000 obj = -1.443262, rho = 0.913910 nSV = 73, nBSV = 71 Total nSV = 73 Accuracy = 64% (64/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 42 nu = 0.720000 obj = -2.046721, rho = 0.876164 nSV = 73, nBSV = 71 Total nSV = 73 Accuracy = 64% (64/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 42 nu = 0.720000 obj = -2.883401, rho = 0.821868 nSV = 73, nBSV = 71 Total nSV = 73 Accuracy = 64% (64/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 40 nu = 0.720000 obj = -4.022022, rho = 0.743766 nSV = 73, nBSV = 71 Total nSV = 73 Accuracy = 64% (64/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 39 nu = 0.720000 obj = -5.525580, rho = 0.631420 nSV = 73, nBSV = 71 Total nSV = 73 Accuracy = 64% (64/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 38 nu = 0.720000 obj = -7.410506, rho = 0.469816 nSV = 73, nBSV = 71 Total nSV = 73 Accuracy = 75% (75/100) (classification) Accuracy = 65.7% (657/1000) (classification) * optimization finished, #iter = 37 nu = 0.720000 obj = -9.546935, rho = 0.237358 nSV = 73, nBSV = 71 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 91.5% (915/1000) (classification) * optimization finished, #iter = 37 nu = 0.660000 obj = -11.845703, rho = 0.109354 nSV = 67, nBSV = 64 Total nSV = 67 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 51 nu = 0.543845 obj = -14.430103, rho = 0.117997 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 32 nu = 0.486911 obj = -17.577532, rho = 0.016043 nSV = 51, nBSV = 48 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 33 nu = 0.412587 obj = -20.611719, rho = 0.079169 nSV = 45, nBSV = 37 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 79 nu = 0.336506 obj = -24.096144, rho = 0.053360 nSV = 39, nBSV = 28 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 72 nu = 0.267187 obj = -28.389535, rho = 0.038430 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.224745 obj = -33.491404, rho = 0.129145 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 47 nu = 0.186016 obj = -39.245347, rho = 0.089430 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 98 nu = 0.151001 obj = -45.382580, rho = 0.081888 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.*.* optimization finished, #iter = 283 nu = 0.119880 obj = -52.451388, rho = -0.006077 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.098238 obj = -60.779267, rho = -0.089379 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -0.859835, rho = -0.948027 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -1.224111, rho = -0.925240 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -1.734507, rho = -0.892461 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -2.440551, rho = -0.845311 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -3.397945, rho = -0.777488 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -4.654654, rho = -0.679927 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 63% (63/100) (classification) Accuracy = 56.6% (566/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -6.213129, rho = -0.539591 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 83% (83/100) (classification) Accuracy = 81.9% (819/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -8.009284, rho = -0.455942 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 92% (92/100) (classification) Accuracy = 91.7% (917/1000) (classification) * optimization finished, #iter = 49 nu = 0.755746 obj = -10.096576, rho = -0.409287 nSV = 78, nBSV = 73 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 94.6% (946/1000) (classification) * optimization finished, #iter = 44 nu = 0.681303 obj = -12.562191, rho = -0.324582 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 48 nu = 0.582094 obj = -15.463372, rho = -0.375406 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 45 nu = 0.504677 obj = -18.925614, rho = -0.266784 nSV = 54, nBSV = 47 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 37 nu = 0.437403 obj = -22.942652, rho = -0.295029 nSV = 45, nBSV = 41 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 63 nu = 0.362564 obj = -27.517158, rho = -0.322867 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 52 nu = 0.300934 obj = -33.308016, rho = -0.399916 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 95 nu = 0.252782 obj = -40.418044, rho = -0.436540 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 139 nu = 0.209142 obj = -49.528190, rho = -0.482961 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..*..* optimization finished, #iter = 437 nu = 0.173736 obj = -61.589963, rho = -0.513807 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 89 nu = 0.155282 obj = -77.840452, rho = -0.521503 nSV = 18, nBSV = 13 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.139287 obj = -95.049761, rho = -0.440262 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -0.876332, rho = 0.868679 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -1.245633, rho = 0.811101 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -1.760895, rho = 0.728278 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -2.469053, rho = 0.609141 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -3.419377, rho = 0.437769 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -4.644996, rho = 0.191260 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 74% (74/100) (classification) Accuracy = 67.4% (674/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -6.115464, rho = -0.163333 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 90% (90/100) (classification) Accuracy = 88.1% (881/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -7.763362, rho = -0.291462 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 94% (94/100) (classification) Accuracy = 93.3% (933/1000) (classification) * optimization finished, #iter = 45 nu = 0.745762 obj = -9.701681, rho = -0.311646 nSV = 76, nBSV = 72 Total nSV = 76 Accuracy = 96% (96/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 40 nu = 0.646873 obj = -11.999637, rho = -0.296762 nSV = 66, nBSV = 61 Total nSV = 66 Accuracy = 96% (96/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 36 nu = 0.557332 obj = -14.817832, rho = -0.277643 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 96% (96/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 40 nu = 0.476908 obj = -18.247915, rho = -0.228938 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 95% (95/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 46 nu = 0.406354 obj = -22.434781, rho = -0.180135 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 95% (95/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 52 nu = 0.342348 obj = -27.925571, rho = -0.194507 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 95% (95/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 46 nu = 0.297328 obj = -34.909438, rho = -0.220508 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 54 nu = 0.257061 obj = -44.081601, rho = -0.283407 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 45 nu = 0.224459 obj = -55.521456, rho = -0.280947 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 49 nu = 0.200602 obj = -69.885589, rho = -0.368251 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 178 nu = 0.178707 obj = -86.285668, rho = -0.390044 nSV = 23, nBSV = 12 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 164 nu = 0.154100 obj = -105.284745, rho = -0.462766 nSV = 21, nBSV = 10 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 49 nu = 0.900000 obj = -0.879287, rho = 0.899796 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 49 nu = 0.900000 obj = -1.251748, rho = 0.855862 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 49 nu = 0.900000 obj = -1.773547, rho = 0.792664 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 49 nu = 0.900000 obj = -2.495230, rho = 0.701758 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 49 nu = 0.900000 obj = -3.473541, rho = 0.570994 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -4.757069, rho = 0.382897 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 58% (58/100) (classification) Accuracy = 57.8% (578/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -6.347359, rho = 0.112328 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 92% (92/100) (classification) Accuracy = 87.6% (876/1000) (classification) * optimization finished, #iter = 49 nu = 0.867899 obj = -8.135095, rho = -0.129545 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 43 nu = 0.789112 obj = -10.168552, rho = -0.227692 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 41 nu = 0.685529 obj = -12.503252, rho = -0.198872 nSV = 70, nBSV = 67 Total nSV = 70 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 35 nu = 0.583607 obj = -15.212715, rho = -0.163638 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 39 nu = 0.502055 obj = -18.498865, rho = -0.096262 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 50 nu = 0.429217 obj = -22.328439, rho = -0.109202 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 42 nu = 0.355415 obj = -26.759848, rho = -0.136183 nSV = 38, nBSV = 33 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 84 nu = 0.292848 obj = -32.105375, rho = -0.158033 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 44 nu = 0.245714 obj = -38.812045, rho = -0.210658 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*....* optimization finished, #iter = 526 nu = 0.206713 obj = -46.812900, rho = -0.254374 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 78 nu = 0.172346 obj = -56.631450, rho = -0.241205 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.144289 obj = -68.662839, rho = -0.264708 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.120836 obj = -83.921411, rho = -0.343140 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 48 nu = 0.860000 obj = -0.840248, rho = 0.922615 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 48 nu = 0.860000 obj = -1.196197, rho = 0.888685 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 48 nu = 0.860000 obj = -1.694892, rho = 0.839879 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 48 nu = 0.860000 obj = -2.384683, rho = 0.769674 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 49 nu = 0.860000 obj = -3.319888, rho = 0.669048 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 50 nu = 0.860000 obj = -4.547149, rho = 0.523425 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 59% (59/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 47 nu = 0.860000 obj = -6.068368, rho = 0.314470 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 82% (82/100) (classification) Accuracy = 78.9% (789/1000) (classification) * optimization finished, #iter = 45 nu = 0.840000 obj = -7.794208, rho = 0.071631 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 96% (96/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 43 nu = 0.760000 obj = -9.662540, rho = 0.036661 nSV = 78, nBSV = 74 Total nSV = 78 Accuracy = 96% (96/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 46 nu = 0.662922 obj = -11.737067, rho = 0.040501 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.549688 obj = -14.098962, rho = 0.011215 nSV = 59, nBSV = 52 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 52 nu = 0.460967 obj = -17.007990, rho = 0.011460 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.382146 obj = -20.560664, rho = -0.024646 nSV = 44, nBSV = 36 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 33 nu = 0.327816 obj = -24.938569, rho = -0.077492 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.279829 obj = -29.948867, rho = -0.146590 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 86 nu = 0.230628 obj = -35.691577, rho = -0.117648 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *..* optimization finished, #iter = 217 nu = 0.197175 obj = -42.647896, rho = -0.078204 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 97 nu = 0.161662 obj = -50.019177, rho = -0.041125 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*..* optimization finished, #iter = 382 nu = 0.134294 obj = -58.605851, rho = 0.035167 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.108391 obj = -68.070032, rho = 0.096982 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -0.840550, rho = 0.921203 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -1.196823, rho = 0.886654 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -1.696189, rho = 0.836958 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 49 nu = 0.860000 obj = -2.387368, rho = 0.765474 nSV = 88, nBSV = 84 Total nSV = 88 Accuracy = 57% (57/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 51 nu = 0.860000 obj = -3.325448, rho = 0.662744 nSV = 88, nBSV = 84 Total nSV = 88 Accuracy = 57% (57/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 53 nu = 0.860000 obj = -4.558654, rho = 0.514978 nSV = 88, nBSV = 84 Total nSV = 88 Accuracy = 59% (59/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 53 nu = 0.860000 obj = -6.092177, rho = 0.302658 nSV = 88, nBSV = 84 Total nSV = 88 Accuracy = 82% (82/100) (classification) Accuracy = 76.6% (766/1000) (classification) * optimization finished, #iter = 45 nu = 0.830288 obj = -7.826948, rho = 0.074984 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 92% (92/100) (classification) Accuracy = 92.3% (923/1000) (classification) * optimization finished, #iter = 41 nu = 0.745871 obj = -9.791995, rho = -0.038922 nSV = 77, nBSV = 74 Total nSV = 77 Accuracy = 98% (98/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 40 nu = 0.671305 obj = -11.969644, rho = -0.080687 nSV = 68, nBSV = 65 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 56 nu = 0.579655 obj = -14.376536, rho = -0.144288 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 90 nu = 0.475554 obj = -17.140327, rho = -0.212262 nSV = 52, nBSV = 44 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 67 nu = 0.402545 obj = -20.352363, rho = -0.208200 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.329596 obj = -24.045414, rho = -0.211869 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 44 nu = 0.273464 obj = -28.432703, rho = -0.215854 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.217045 obj = -33.659966, rho = -0.209105 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 63 nu = 0.179923 obj = -40.321730, rho = -0.136886 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 69 nu = 0.153156 obj = -48.066597, rho = -0.060574 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *..* optimization finished, #iter = 200 nu = 0.125876 obj = -56.537582, rho = 0.020324 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..* optimization finished, #iter = 229 nu = 0.101909 obj = -66.713933, rho = 0.048866 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -0.909347, rho = -0.940423 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.288719, rho = -0.914302 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.813756, rho = -0.876727 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.526230, rho = -0.822678 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.462597, rho = -0.744932 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 59% (59/100) (classification) Accuracy = 51.9% (519/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -4.626417, rho = -0.633097 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 79% (79/100) (classification) Accuracy = 74.6% (746/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -5.985008, rho = -0.542399 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 91% (91/100) (classification) Accuracy = 87% (870/1000) (classification) * optimization finished, #iter = 43 nu = 0.824516 obj = -7.519102, rho = -0.475918 nSV = 84, nBSV = 82 Total nSV = 84 Accuracy = 95% (95/100) (classification) Accuracy = 92.7% (927/1000) (classification) * optimization finished, #iter = 40 nu = 0.717967 obj = -9.336760, rho = -0.443473 nSV = 73, nBSV = 69 Total nSV = 73 Accuracy = 95% (95/100) (classification) Accuracy = 94.7% (947/1000) (classification) * optimization finished, #iter = 36 nu = 0.625028 obj = -11.547659, rho = -0.367962 nSV = 64, nBSV = 62 Total nSV = 64 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 45 nu = 0.545467 obj = -14.048479, rho = -0.349885 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 31 nu = 0.469897 obj = -16.808301, rho = -0.295111 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 73 nu = 0.391221 obj = -19.888094, rho = -0.351980 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 98 nu = 0.317868 obj = -23.599029, rho = -0.337085 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 42 nu = 0.267018 obj = -27.984334, rho = -0.501857 nSV = 32, nBSV = 23 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 90 nu = 0.216733 obj = -32.886137, rho = -0.485766 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 47 nu = 0.177803 obj = -38.739287, rho = -0.467428 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 49 nu = 0.153373 obj = -45.385200, rho = -0.364113 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.*..* optimization finished, #iter = 294 nu = 0.120609 obj = -51.825759, rho = -0.313850 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 38 nu = 0.098246 obj = -59.695253, rho = -0.330069 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -0.878715, rho = -0.930825 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -1.250565, rho = -0.900719 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 55% (55/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -1.771100, rho = -0.857189 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 55% (55/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -2.490167, rho = -0.794574 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 55% (55/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 49 nu = 0.900000 obj = -3.463067, rho = -0.704304 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 55% (55/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 49 nu = 0.900000 obj = -4.735396, rho = -0.574655 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 59% (59/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 50 nu = 0.900000 obj = -6.302516, rho = -0.388328 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 86% (86/100) (classification) Accuracy = 81.9% (819/1000) (classification) * optimization finished, #iter = 50 nu = 0.862154 obj = -8.066884, rho = -0.215997 nSV = 90, nBSV = 85 Total nSV = 90 Accuracy = 97% (97/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 42 nu = 0.792309 obj = -10.042680, rho = -0.078188 nSV = 82, nBSV = 78 Total nSV = 82 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 40 nu = 0.683044 obj = -12.220264, rho = -0.123203 nSV = 72, nBSV = 67 Total nSV = 72 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.576963 obj = -14.701452, rho = -0.183209 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.480052 obj = -17.733887, rho = -0.152040 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.400708 obj = -21.479836, rho = -0.224998 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 60 nu = 0.335851 obj = -26.178748, rho = -0.268514 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 51 nu = 0.288256 obj = -31.918579, rho = -0.322180 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 60 nu = 0.245498 obj = -38.492060, rho = -0.454159 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 39 nu = 0.205251 obj = -46.359318, rho = -0.377824 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 98 nu = 0.174457 obj = -55.106562, rho = -0.118703 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 189 nu = 0.142923 obj = -65.454199, rho = -0.099434 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 67 nu = 0.116018 obj = -78.528905, rho = -0.124300 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -0.931609, rho = 0.885946 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.322166, rho = 0.835940 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.864820, rho = 0.764007 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.605788, rho = 0.660537 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.589672, rho = 0.511699 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 53% (53/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.835348, rho = 0.297603 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 74% (74/100) (classification) Accuracy = 68.6% (686/1000) (classification) * optimization finished, #iter = 53 nu = 0.944246 obj = -6.278781, rho = 0.043117 nSV = 96, nBSV = 93 Total nSV = 96 Accuracy = 93% (93/100) (classification) Accuracy = 90.4% (904/1000) (classification) * optimization finished, #iter = 46 nu = 0.861092 obj = -7.853276, rho = -0.123479 nSV = 89, nBSV = 85 Total nSV = 89 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 40 nu = 0.763767 obj = -9.719193, rho = -0.043415 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 35 nu = 0.680000 obj = -11.722672, rho = -0.015003 nSV = 69, nBSV = 67 Total nSV = 69 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.555149 obj = -13.870994, rho = 0.012579 nSV = 60, nBSV = 52 Total nSV = 60 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 98 nu = 0.470272 obj = -16.475153, rho = -0.059940 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 53 nu = 0.382792 obj = -19.318895, rho = -0.059897 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 49 nu = 0.314639 obj = -22.608080, rho = -0.155594 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 73 nu = 0.251167 obj = -26.554312, rho = -0.145987 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 81 nu = 0.205489 obj = -31.316352, rho = -0.044427 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 58 nu = 0.167674 obj = -37.397223, rho = -0.153612 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 99 nu = 0.142217 obj = -44.328437, rho = -0.209130 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.113875 obj = -52.631129, rho = -0.226310 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 64 nu = 0.092319 obj = -63.463532, rho = -0.232252 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -0.878139, rho = 0.886237 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 52.2% (522/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -1.249371, rho = 0.836358 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 52.2% (522/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -1.768629, rho = 0.764609 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 52.2% (522/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -2.485054, rho = 0.661402 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 52.2% (522/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -3.452485, rho = 0.512944 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 52.2% (522/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -4.713500, rho = 0.299395 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 68% (68/100) (classification) Accuracy = 62.7% (627/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -6.257210, rho = -0.007786 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 85% (85/100) (classification) Accuracy = 86% (860/1000) (classification) * optimization finished, #iter = 45 nu = 0.846161 obj = -8.023138, rho = -0.170050 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 93% (93/100) (classification) Accuracy = 94.3% (943/1000) (classification) * optimization finished, #iter = 48 nu = 0.761206 obj = -10.119247, rho = -0.144128 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 95% (95/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 47 nu = 0.680435 obj = -12.532445, rho = -0.148339 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 51 nu = 0.590846 obj = -15.343960, rho = -0.153091 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 45 nu = 0.499914 obj = -18.627934, rho = -0.165155 nSV = 53, nBSV = 45 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.413941 obj = -22.856878, rho = -0.134167 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 37 nu = 0.364533 obj = -28.145919, rho = -0.135460 nSV = 38, nBSV = 34 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 61 nu = 0.307810 obj = -34.124836, rho = -0.020525 nSV = 35, nBSV = 25 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 76 nu = 0.256808 obj = -41.801985, rho = 0.043358 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 69 nu = 0.216346 obj = -51.762786, rho = -0.030967 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 58 nu = 0.181265 obj = -65.112620, rho = -0.030122 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 87 nu = 0.160276 obj = -81.694441, rho = -0.153188 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 99 nu = 0.140232 obj = -103.326284, rho = -0.235766 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -0.921707, rho = -0.921331 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 54.1% (541/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.314291, rho = -0.886839 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 54.1% (541/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.866671, rho = -0.837002 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 54.1% (541/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.635718, rho = -0.765536 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 54.1% (541/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.689144, rho = -0.662736 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 54.1% (541/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -5.095173, rho = -0.514862 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 54.7% (547/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -6.891579, rho = -0.302153 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 81% (81/100) (classification) Accuracy = 81.5% (815/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -9.007794, rho = 0.003818 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -11.317922, rho = 0.067228 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 48 nu = 0.756977 obj = -13.967610, rho = -0.008955 nSV = 78, nBSV = 74 Total nSV = 78 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 40 nu = 0.651983 obj = -17.133220, rho = -0.046930 nSV = 67, nBSV = 63 Total nSV = 67 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 45 nu = 0.560784 obj = -20.828881, rho = -0.042132 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 91 nu = 0.463082 obj = -25.368123, rho = -0.061949 nSV = 50, nBSV = 42 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 89 nu = 0.395893 obj = -31.307808, rho = -0.073099 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 86 nu = 0.334970 obj = -38.692127, rho = 0.009466 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 40 nu = 0.285977 obj = -48.339291, rho = -0.016074 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 38 nu = 0.248988 obj = -60.277535, rho = -0.096393 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 52 nu = 0.220000 obj = -75.220956, rho = -0.030693 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 71 nu = 0.203482 obj = -89.777281, rho = 0.318545 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *..* optimization finished, #iter = 204 nu = 0.165660 obj = -103.849689, rho = 0.270401 nSV = 21, nBSV = 10 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -0.946696, rho = 0.844842 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 48.1% (481/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.340770, rho = 0.776814 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 48.1% (481/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.885169, rho = 0.678958 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 48.1% (481/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -2.621793, rho = 0.538197 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 48.1% (481/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -3.585245, rho = 0.335719 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 61% (61/100) (classification) Accuracy = 60.2% (602/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.772185, rho = 0.044465 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 96% (96/100) (classification) Accuracy = 93% (930/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.097257, rho = -0.184262 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 96% (96/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -7.593161, rho = -0.111457 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 40 nu = 0.747372 obj = -9.296987, rho = -0.087745 nSV = 77, nBSV = 74 Total nSV = 77 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 44 nu = 0.642339 obj = -11.145625, rho = -0.075624 nSV = 66, nBSV = 61 Total nSV = 66 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 45 nu = 0.529036 obj = -13.293153, rho = -0.090507 nSV = 56, nBSV = 50 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 47 nu = 0.446904 obj = -15.759212, rho = -0.046538 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 51 nu = 0.368674 obj = -18.480070, rho = -0.104999 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 81 nu = 0.308158 obj = -21.445625, rho = -0.198230 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 88 nu = 0.247661 obj = -24.527603, rho = -0.166356 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 88 nu = 0.194932 obj = -27.996427, rho = -0.175224 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 53 nu = 0.163078 obj = -31.827010, rho = -0.207631 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 179 nu = 0.125255 obj = -35.169029, rho = -0.173406 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.098518 obj = -38.475541, rho = -0.358385 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.074910 obj = -41.479893, rho = -0.378350 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -0.923391, rho = 0.840986 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.305162, rho = 0.771266 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.829636, rho = 0.670978 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.532988, rho = 0.526718 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -3.439039, rho = 0.319602 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 64% (64/100) (classification) Accuracy = 60.5% (605/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -4.523669, rho = 0.021281 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 95% (95/100) (classification) Accuracy = 87.7% (877/1000) (classification) * optimization finished, #iter = 51 nu = 0.911824 obj = -5.684353, rho = -0.211836 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 41 nu = 0.785697 obj = -6.994371, rho = -0.152921 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 41 nu = 0.695793 obj = -8.492065, rho = -0.188253 nSV = 71, nBSV = 68 Total nSV = 71 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 40 nu = 0.586408 obj = -10.116764, rho = -0.157812 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 65 nu = 0.492215 obj = -11.930484, rho = -0.108528 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 65 nu = 0.400551 obj = -13.930662, rho = -0.133824 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 34 nu = 0.324026 obj = -16.404771, rho = -0.125326 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 42 nu = 0.270591 obj = -19.193475, rho = -0.112138 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 44 nu = 0.220000 obj = -22.300127, rho = -0.121231 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.178179 obj = -25.431935, rho = -0.199197 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 46 nu = 0.143903 obj = -28.996098, rho = -0.282875 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.112846 obj = -32.723080, rho = -0.345446 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 49 nu = 0.091317 obj = -36.793444, rho = -0.259933 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 53 nu = 0.073083 obj = -39.670496, rho = -0.218741 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -0.888649, rho = -0.929485 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.1% (481/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.258504, rho = -0.898568 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.1% (481/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.769382, rho = -0.854095 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.1% (481/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -2.460512, rho = -0.790123 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.1% (481/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -3.364162, rho = -0.698102 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 67% (67/100) (classification) Accuracy = 56.4% (564/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -4.476745, rho = -0.565735 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 91% (91/100) (classification) Accuracy = 79.5% (795/1000) (classification) * optimization finished, #iter = 44 nu = 0.870909 obj = -5.754172, rho = -0.474084 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 96% (96/100) (classification) Accuracy = 89.4% (894/1000) (classification) * optimization finished, #iter = 41 nu = 0.797093 obj = -7.238544, rho = -0.478077 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 97% (97/100) (classification) Accuracy = 93% (930/1000) (classification) * optimization finished, #iter = 49 nu = 0.700268 obj = -8.935432, rho = -0.435623 nSV = 72, nBSV = 68 Total nSV = 72 Accuracy = 99% (99/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 35 nu = 0.613847 obj = -10.913789, rho = -0.397154 nSV = 62, nBSV = 59 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 32 nu = 0.518915 obj = -13.155202, rho = -0.393553 nSV = 53, nBSV = 50 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 36 nu = 0.441182 obj = -15.708658, rho = -0.393550 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 40 nu = 0.366297 obj = -18.545260, rho = -0.397402 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 31 nu = 0.301652 obj = -21.732308, rho = -0.388599 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 70 nu = 0.246409 obj = -25.326919, rho = -0.361321 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 92 nu = 0.201173 obj = -29.634491, rho = -0.425069 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 161 nu = 0.162794 obj = -34.650632, rho = -0.442466 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.137399 obj = -40.279360, rho = -0.637267 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.111800 obj = -45.577996, rho = -0.801611 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*.* optimization finished, #iter = 223 nu = 0.084967 obj = -51.015070, rho = -0.822519 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -0.859375, rho = -0.936057 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -1.223161, rho = -0.908021 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -1.732540, rho = -0.867692 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -2.436482, rho = -0.809682 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -3.389525, rho = -0.726237 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -4.637233, rho = -0.606206 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 64% (64/100) (classification) Accuracy = 57.5% (575/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -6.177083, rho = -0.433547 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 88% (88/100) (classification) Accuracy = 86.5% (865/1000) (classification) * optimization finished, #iter = 50 nu = 0.845374 obj = -7.903129, rho = -0.350908 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 92% (92/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 58 nu = 0.748751 obj = -9.858913, rho = -0.253795 nSV = 78, nBSV = 72 Total nSV = 78 Accuracy = 96% (96/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.660000 obj = -12.271085, rho = -0.242667 nSV = 68, nBSV = 65 Total nSV = 68 Accuracy = 96% (96/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 62 nu = 0.563756 obj = -15.141500, rho = -0.215360 nSV = 60, nBSV = 52 Total nSV = 60 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.489995 obj = -18.774883, rho = -0.269274 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.420896 obj = -23.167974, rho = -0.298419 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 57 nu = 0.355034 obj = -28.644520, rho = -0.327678 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.307413 obj = -35.722892, rho = -0.458680 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 37 nu = 0.274645 obj = -44.151859, rho = -0.317076 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 57 nu = 0.228850 obj = -54.128299, rho = -0.401193 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 61 nu = 0.197954 obj = -66.962699, rho = -0.458030 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 154 nu = 0.165393 obj = -82.686468, rho = -0.452817 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 90 nu = 0.138973 obj = -104.526136, rho = -0.464821 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 37 nu = 0.700000 obj = -0.689458, rho = -0.972002 nSV = 71, nBSV = 69 Total nSV = 71 Accuracy = 65% (65/100) (classification) Accuracy = 52.1% (521/1000) (classification) * optimization finished, #iter = 42 nu = 0.700000 obj = -0.985114, rho = -0.959859 nSV = 71, nBSV = 68 Total nSV = 71 Accuracy = 65% (65/100) (classification) Accuracy = 52.1% (521/1000) (classification) * optimization finished, #iter = 42 nu = 0.700000 obj = -1.403289, rho = -0.942259 nSV = 71, nBSV = 68 Total nSV = 71 Accuracy = 65% (65/100) (classification) Accuracy = 52.1% (521/1000) (classification) * optimization finished, #iter = 43 nu = 0.700000 obj = -1.990113, rho = -0.916406 nSV = 72, nBSV = 68 Total nSV = 72 Accuracy = 65% (65/100) (classification) Accuracy = 52.1% (521/1000) (classification) * optimization finished, #iter = 43 nu = 0.700000 obj = -2.803814, rho = -0.879755 nSV = 72, nBSV = 68 Total nSV = 72 Accuracy = 65% (65/100) (classification) Accuracy = 52.1% (521/1000) (classification) * optimization finished, #iter = 43 nu = 0.700000 obj = -3.911348, rho = -0.827033 nSV = 72, nBSV = 68 Total nSV = 72 Accuracy = 65% (65/100) (classification) Accuracy = 52.1% (521/1000) (classification) * optimization finished, #iter = 45 nu = 0.700000 obj = -5.374268, rho = -0.751102 nSV = 72, nBSV = 67 Total nSV = 72 Accuracy = 65% (65/100) (classification) Accuracy = 52.6% (526/1000) (classification) * optimization finished, #iter = 45 nu = 0.700000 obj = -7.209161, rho = -0.641972 nSV = 72, nBSV = 67 Total nSV = 72 Accuracy = 77% (77/100) (classification) Accuracy = 66.7% (667/1000) (classification) * optimization finished, #iter = 44 nu = 0.700000 obj = -9.291060, rho = -0.485340 nSV = 72, nBSV = 67 Total nSV = 72 Accuracy = 94% (94/100) (classification) Accuracy = 90% (900/1000) (classification) * optimization finished, #iter = 43 nu = 0.626176 obj = -11.524084, rho = -0.397110 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 98% (98/100) (classification) Accuracy = 94.7% (947/1000) (classification) * optimization finished, #iter = 61 nu = 0.542994 obj = -14.014595, rho = -0.339841 nSV = 57, nBSV = 51 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 40 nu = 0.461704 obj = -16.983800, rho = -0.344006 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 55 nu = 0.396853 obj = -20.358760, rho = -0.314745 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 56 nu = 0.324960 obj = -24.279395, rho = -0.299513 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 81 nu = 0.266399 obj = -29.030070, rho = -0.330626 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 61 nu = 0.232515 obj = -34.825119, rho = -0.218425 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 83 nu = 0.189875 obj = -40.692239, rho = -0.234182 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.152556 obj = -47.712176, rho = -0.169754 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 197 nu = 0.126091 obj = -55.933210, rho = -0.161121 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.105709 obj = -65.441348, rho = -0.134455 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -0.933565, rho = 0.877854 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 47.2% (472/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.326213, rho = 0.824299 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 47.2% (472/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.873194, rho = 0.747263 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 47.2% (472/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.623115, rho = 0.636451 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 47.2% (472/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.625524, rho = 0.477052 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 53% (53/100) (classification) Accuracy = 47.4% (474/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.909532, rho = 0.247766 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 78% (78/100) (classification) Accuracy = 71.4% (714/1000) (classification) * optimization finished, #iter = 48 nu = 0.948645 obj = -6.431903, rho = 0.012530 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 91% (91/100) (classification) Accuracy = 93.4% (934/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -8.171818, rho = -0.061005 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 96% (96/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 45 nu = 0.786677 obj = -10.144333, rho = -0.022738 nSV = 81, nBSV = 77 Total nSV = 81 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 53 nu = 0.678427 obj = -12.355763, rho = 0.061387 nSV = 71, nBSV = 65 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.577536 obj = -15.100644, rho = 0.025084 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 35 nu = 0.495254 obj = -18.353749, rho = 0.048534 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 34 nu = 0.434224 obj = -22.072751, rho = 0.067896 nSV = 44, nBSV = 41 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 52 nu = 0.350755 obj = -26.141129, rho = 0.133872 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 70 nu = 0.286078 obj = -31.368611, rho = 0.114912 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 55 nu = 0.237917 obj = -38.088921, rho = 0.246318 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 68 nu = 0.203735 obj = -46.266057, rho = 0.187645 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 60 nu = 0.168797 obj = -56.032703, rho = 0.183755 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 87 nu = 0.144975 obj = -68.335330, rho = 0.252369 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 137 nu = 0.122484 obj = -82.154287, rho = 0.322776 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 45 nu = 0.840000 obj = -0.822654, rho = 0.916137 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 45 nu = 0.840000 obj = -1.172406, rho = 0.879367 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 44 nu = 0.840000 obj = -1.663811, rho = 0.826476 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 44 nu = 0.840000 obj = -2.346471, rho = 0.750395 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 44 nu = 0.840000 obj = -3.278366, rho = 0.640955 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 44 nu = 0.840000 obj = -4.515236, rho = 0.483532 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -6.080018, rho = 0.257087 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 80% (80/100) (classification) Accuracy = 72.7% (727/1000) (classification) * optimization finished, #iter = 47 nu = 0.832001 obj = -7.888723, rho = -0.037832 nSV = 86, nBSV = 82 Total nSV = 86 Accuracy = 100% (100/100) (classification) Accuracy = 94.1% (941/1000) (classification) * optimization finished, #iter = 43 nu = 0.765328 obj = -9.910322, rho = -0.107536 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 54 nu = 0.664590 obj = -12.185986, rho = -0.097919 nSV = 69, nBSV = 64 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 41 nu = 0.565151 obj = -14.964211, rho = -0.101341 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 30 nu = 0.483155 obj = -18.485766, rho = -0.108313 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 46 nu = 0.424545 obj = -22.540404, rho = -0.144427 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.354484 obj = -27.177112, rho = -0.083908 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 38 nu = 0.305438 obj = -32.805045, rho = 0.035479 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 37 nu = 0.255558 obj = -39.207220, rho = 0.031729 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 90 nu = 0.205980 obj = -46.906911, rho = -0.015116 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 49 nu = 0.173051 obj = -57.092746, rho = -0.092546 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.152592 obj = -68.577301, rho = 0.010443 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 82 nu = 0.124173 obj = -80.618710, rho = -0.015219 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.967774, rho = -0.056928 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 92% (92/100) (classification) Accuracy = 90.4% (904/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.371770, rho = -0.081888 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 92% (92/100) (classification) Accuracy = 90.4% (904/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.931168, rho = -0.117792 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 92% (92/100) (classification) Accuracy = 90.4% (904/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.690872, rho = -0.169438 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 92% (92/100) (classification) Accuracy = 90.4% (904/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.690636, rho = -0.243728 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 92% (92/100) (classification) Accuracy = 90.4% (904/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.943881, rho = -0.326607 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 92% (92/100) (classification) Accuracy = 90.9% (909/1000) (classification) * optimization finished, #iter = 48 nu = 0.942472 obj = -6.465618, rho = -0.269081 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 92% (92/100) (classification) Accuracy = 93.9% (939/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -8.306407, rho = -0.353711 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 94% (94/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 42 nu = 0.783731 obj = -10.481062, rho = -0.355778 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 96% (96/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 49 nu = 0.687351 obj = -13.161585, rho = -0.333300 nSV = 71, nBSV = 66 Total nSV = 71 Accuracy = 96% (96/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 44 nu = 0.607643 obj = -16.479927, rho = -0.282989 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 96% (96/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 39 nu = 0.531732 obj = -20.460087, rho = -0.243017 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 96% (96/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 37 nu = 0.455311 obj = -25.290843, rho = -0.258902 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 62 nu = 0.384843 obj = -31.365282, rho = -0.276114 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 96% (96/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 39 nu = 0.339915 obj = -39.120213, rho = -0.413177 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 96% (96/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 92 nu = 0.288105 obj = -48.644728, rho = -0.526887 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 96% (96/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 84 nu = 0.249028 obj = -60.882124, rho = -0.477567 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 96% (96/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 73 nu = 0.211091 obj = -77.369915, rho = -0.519731 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 96% (96/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.184926 obj = -99.764169, rho = -0.485356 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 96% (96/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 284 nu = 0.167463 obj = -128.740363, rho = -0.372334 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -0.858448, rho = -0.949779 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -1.221241, rho = -0.927760 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 49 nu = 0.880000 obj = -1.728573, rho = -0.895858 nSV = 90, nBSV = 86 Total nSV = 90 Accuracy = 56% (56/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 48 nu = 0.880000 obj = -2.428274, rho = -0.850045 nSV = 91, nBSV = 86 Total nSV = 91 Accuracy = 56% (56/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 49 nu = 0.880000 obj = -3.372545, rho = -0.784518 nSV = 91, nBSV = 86 Total nSV = 91 Accuracy = 56% (56/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 51 nu = 0.880000 obj = -4.602104, rho = -0.689608 nSV = 91, nBSV = 86 Total nSV = 91 Accuracy = 61% (61/100) (classification) Accuracy = 54.7% (547/1000) (classification) * optimization finished, #iter = 51 nu = 0.880000 obj = -6.104397, rho = -0.553517 nSV = 91, nBSV = 86 Total nSV = 91 Accuracy = 83% (83/100) (classification) Accuracy = 81% (810/1000) (classification) * optimization finished, #iter = 46 nu = 0.840000 obj = -7.771825, rho = -0.418494 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 98% (98/100) (classification) Accuracy = 93.5% (935/1000) (classification) * optimization finished, #iter = 44 nu = 0.771073 obj = -9.635816, rho = -0.325116 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 42 nu = 0.658963 obj = -11.616984, rho = -0.274339 nSV = 68, nBSV = 64 Total nSV = 68 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 36 nu = 0.551001 obj = -13.930388, rho = -0.244865 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 35 nu = 0.466154 obj = -16.592465, rho = -0.186838 nSV = 50, nBSV = 43 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 89 nu = 0.386129 obj = -19.510557, rho = -0.145327 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 29 nu = 0.316049 obj = -23.081407, rho = -0.192810 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 36 nu = 0.266397 obj = -26.748902, rho = -0.166021 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 54 nu = 0.214047 obj = -30.864116, rho = -0.137527 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.175277 obj = -35.513604, rho = -0.168421 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 74 nu = 0.143427 obj = -39.619541, rho = -0.160021 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.106876 obj = -43.657514, rho = -0.209408 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.083825 obj = -48.062307, rho = -0.293452 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -0.877944, rho = 0.913437 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -1.248968, rho = 0.875484 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -1.767795, rho = 0.820890 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -2.483329, rho = 0.742359 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -3.448916, rho = 0.629397 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -4.706117, rho = 0.466906 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 57% (57/100) (classification) Accuracy = 56.7% (567/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -6.241933, rho = 0.233171 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 90% (90/100) (classification) Accuracy = 87.4% (874/1000) (classification) * optimization finished, #iter = 46 nu = 0.871326 obj = -7.925191, rho = 0.051872 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 40 nu = 0.779581 obj = -9.706699, rho = -0.059099 nSV = 78, nBSV = 76 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 37 nu = 0.660000 obj = -11.727596, rho = -0.135016 nSV = 68, nBSV = 65 Total nSV = 68 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 37 nu = 0.551869 obj = -14.137769, rho = -0.167644 nSV = 57, nBSV = 51 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 34 nu = 0.458873 obj = -17.148928, rho = -0.181445 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 36 nu = 0.384875 obj = -20.859847, rho = -0.160285 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 37 nu = 0.329788 obj = -25.431358, rho = -0.131140 nSV = 35, nBSV = 30 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 34 nu = 0.283737 obj = -30.584924, rho = -0.155869 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.238330 obj = -36.409689, rho = -0.043323 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 90 nu = 0.196807 obj = -43.179961, rho = 0.019827 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.161400 obj = -50.939882, rho = 0.028668 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.132640 obj = -60.202772, rho = -0.047224 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 62 nu = 0.105038 obj = -72.516315, rho = -0.069487 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -0.929052, rho = 0.847848 nSV = 97, nBSV = 94 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -1.316877, rho = 0.781137 nSV = 97, nBSV = 94 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 51 nu = 0.960000 obj = -1.853878, rho = 0.684947 nSV = 97, nBSV = 94 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 51 nu = 0.960000 obj = -2.583147, rho = 0.546811 nSV = 97, nBSV = 94 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 52 nu = 0.960000 obj = -3.542828, rho = 0.347472 nSV = 97, nBSV = 94 Total nSV = 97 Accuracy = 55% (55/100) (classification) Accuracy = 55.2% (552/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -4.738422, rho = 0.061124 nSV = 97, nBSV = 94 Total nSV = 97 Accuracy = 81% (81/100) (classification) Accuracy = 79.4% (794/1000) (classification) * optimization finished, #iter = 63 nu = 0.937455 obj = -6.081265, rho = -0.272126 nSV = 96, nBSV = 92 Total nSV = 96 Accuracy = 96% (96/100) (classification) Accuracy = 92.4% (924/1000) (classification) * optimization finished, #iter = 43 nu = 0.840185 obj = -7.618295, rho = -0.309158 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 98% (98/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 41 nu = 0.730943 obj = -9.412409, rho = -0.280440 nSV = 75, nBSV = 71 Total nSV = 75 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 38 nu = 0.640000 obj = -11.581840, rho = -0.348779 nSV = 65, nBSV = 62 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 54 nu = 0.554707 obj = -13.962984, rho = -0.290087 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 44 nu = 0.464189 obj = -16.689439, rho = -0.305376 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 45 nu = 0.382354 obj = -19.936272, rho = -0.359113 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 27 nu = 0.313906 obj = -23.977842, rho = -0.431172 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 21 nu = 0.263997 obj = -29.172655, rho = -0.433123 nSV = 29, nBSV = 25 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 38 nu = 0.224672 obj = -34.869478, rho = -0.502478 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 32 nu = 0.191131 obj = -41.578895, rho = -0.637379 nSV = 22, nBSV = 17 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 97 nu = 0.159808 obj = -48.090134, rho = -0.647929 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*..* optimization finished, #iter = 326 nu = 0.129348 obj = -55.031972, rho = -0.663314 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 77 nu = 0.106541 obj = -61.939145, rho = -0.667501 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.969128, rho = -0.023049 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 99% (99/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.374572, rho = -0.033155 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 99% (99/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.936966, rho = -0.047692 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 99% (99/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.702870, rho = -0.068603 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 99% (99/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.715461, rho = -0.098682 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 99% (99/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -4.987617, rho = -0.141949 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 99% (99/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 49 nu = 0.976124 obj = -6.458560, rho = -0.119273 nSV = 98, nBSV = 96 Total nSV = 98 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 45 nu = 0.898689 obj = -8.115239, rho = -0.109854 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 46 nu = 0.787022 obj = -9.995512, rho = -0.113217 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 39 nu = 0.676630 obj = -12.198717, rho = -0.071080 nSV = 69, nBSV = 66 Total nSV = 69 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.579632 obj = -14.736133, rho = 0.017761 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 40 nu = 0.494710 obj = -17.586179, rho = -0.058167 nSV = 53, nBSV = 46 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 41 nu = 0.410996 obj = -20.822714, rho = -0.037577 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 81 nu = 0.335474 obj = -24.574985, rho = -0.005319 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.275177 obj = -29.169264, rho = -0.055049 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.227771 obj = -34.827850, rho = -0.084427 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 60 nu = 0.190354 obj = -41.362381, rho = -0.075064 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *..* optimization finished, #iter = 206 nu = 0.154710 obj = -48.751994, rho = -0.037327 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 137 nu = 0.125996 obj = -58.093571, rho = -0.131712 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.105224 obj = -69.346354, rho = -0.173786 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 47 nu = 0.800000 obj = -0.786599, rho = 0.945396 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 47 nu = 0.800000 obj = -1.123031, rho = 0.921455 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 47 nu = 0.800000 obj = -1.597935, rho = 0.887017 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 47 nu = 0.800000 obj = -2.262362, rho = 0.838026 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 47 nu = 0.800000 obj = -3.179420, rho = 0.767009 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 49 nu = 0.800000 obj = -4.418517, rho = 0.665053 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 47 nu = 0.800000 obj = -6.035253, rho = 0.517718 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 65% (65/100) (classification) Accuracy = 54.9% (549/1000) (classification) * optimization finished, #iter = 47 nu = 0.800000 obj = -8.018132, rho = 0.306948 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 85% (85/100) (classification) Accuracy = 84.6% (846/1000) (classification) * optimization finished, #iter = 43 nu = 0.760655 obj = -10.229768, rho = 0.119648 nSV = 78, nBSV = 74 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 94.7% (947/1000) (classification) * optimization finished, #iter = 39 nu = 0.688291 obj = -12.816357, rho = 0.097158 nSV = 71, nBSV = 68 Total nSV = 71 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 45 nu = 0.599476 obj = -15.739104, rho = 0.059057 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 44 nu = 0.510657 obj = -19.326612, rho = 0.111700 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 44 nu = 0.448178 obj = -23.450596, rho = 0.070298 nSV = 48, nBSV = 44 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 51 nu = 0.370625 obj = -28.172743, rho = 0.067900 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.310632 obj = -33.990439, rho = 0.043817 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 33 nu = 0.264027 obj = -40.641499, rho = 0.238482 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 96 nu = 0.223353 obj = -47.762123, rho = 0.371374 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 185 nu = 0.182983 obj = -55.568967, rho = 0.385540 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.146044 obj = -64.800214, rho = 0.353192 nSV = 21, nBSV = 10 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 140 nu = 0.120055 obj = -75.777952, rho = 0.285964 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -0.892213, rho = 0.891683 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -1.265878, rho = 0.844192 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -1.784641, rho = 0.775877 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -2.492086, rho = 0.677611 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -3.429491, rho = 0.536259 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -4.611920, rho = 0.332932 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 82% (82/100) (classification) Accuracy = 70.7% (707/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -5.969345, rho = 0.040457 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 96% (96/100) (classification) Accuracy = 93.9% (939/1000) (classification) * optimization finished, #iter = 44 nu = 0.840000 obj = -7.394936, rho = 0.041873 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 97% (97/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 48 nu = 0.723120 obj = -8.954638, rho = -0.005573 nSV = 75, nBSV = 70 Total nSV = 75 Accuracy = 97% (97/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 40 nu = 0.604339 obj = -10.821049, rho = 0.013599 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 40 nu = 0.507226 obj = -13.067870, rho = -0.026498 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 34 nu = 0.432950 obj = -15.789333, rho = -0.032984 nSV = 45, nBSV = 42 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 60 nu = 0.363585 obj = -18.916546, rho = -0.035559 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 43 nu = 0.302102 obj = -22.710988, rho = -0.026076 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 76 nu = 0.252481 obj = -27.116880, rho = -0.008442 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 82 nu = 0.207700 obj = -32.569263, rho = 0.005722 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 63 nu = 0.180072 obj = -38.919374, rho = -0.068518 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.146783 obj = -45.465488, rho = -0.056534 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.119035 obj = -53.335624, rho = 0.030150 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 93 nu = 0.097641 obj = -62.854737, rho = -0.011771 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 42 nu = 0.780000 obj = -0.764497, rho = 0.937091 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 61% (61/100) (classification) Accuracy = 46.6% (466/1000) (classification) * optimization finished, #iter = 42 nu = 0.780000 obj = -1.089912, rho = 0.909509 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 61% (61/100) (classification) Accuracy = 46.6% (466/1000) (classification) * optimization finished, #iter = 42 nu = 0.780000 obj = -1.547552, rho = 0.869833 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 61% (61/100) (classification) Accuracy = 46.6% (466/1000) (classification) * optimization finished, #iter = 42 nu = 0.780000 obj = -2.184214, rho = 0.812761 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 61% (61/100) (classification) Accuracy = 46.6% (466/1000) (classification) * optimization finished, #iter = 41 nu = 0.780000 obj = -3.055264, rho = 0.730666 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 61% (61/100) (classification) Accuracy = 46.6% (466/1000) (classification) * optimization finished, #iter = 41 nu = 0.780000 obj = -4.215618, rho = 0.612577 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 61% (61/100) (classification) Accuracy = 46.7% (467/1000) (classification) * optimization finished, #iter = 41 nu = 0.780000 obj = -5.693112, rho = 0.442712 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 73% (73/100) (classification) Accuracy = 60.2% (602/1000) (classification) * optimization finished, #iter = 41 nu = 0.780000 obj = -7.421929, rho = 0.198369 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 93% (93/100) (classification) Accuracy = 88.8% (888/1000) (classification) * optimization finished, #iter = 44 nu = 0.720731 obj = -9.234848, rho = 0.069486 nSV = 74, nBSV = 71 Total nSV = 74 Accuracy = 96% (96/100) (classification) Accuracy = 94.7% (947/1000) (classification) * optimization finished, #iter = 66 nu = 0.619858 obj = -11.314778, rho = 0.000923 nSV = 65, nBSV = 60 Total nSV = 65 Accuracy = 96% (96/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 32 nu = 0.539472 obj = -13.794581, rho = -0.023902 nSV = 55, nBSV = 52 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 48 nu = 0.452405 obj = -16.593016, rho = -0.019736 nSV = 49, nBSV = 42 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 35 nu = 0.381228 obj = -19.962714, rho = -0.056113 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 53 nu = 0.320985 obj = -23.797333, rho = -0.044679 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.270068 obj = -28.056182, rho = -0.099654 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *..* optimization finished, #iter = 224 nu = 0.218755 obj = -32.977592, rho = -0.073704 nSV = 27, nBSV = 17 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.177303 obj = -39.106079, rho = -0.071303 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .**.* optimization finished, #iter = 187 nu = 0.145899 obj = -46.317290, rho = -0.067156 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.119872 obj = -55.535007, rho = -0.141309 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.098160 obj = -67.255516, rho = -0.192154 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.800000 obj = -0.783461, rho = -0.960214 nSV = 80, nBSV = 80 Total nSV = 80 Accuracy = 60% (60/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 42 nu = 0.800000 obj = -1.116538, rho = -0.942770 nSV = 80, nBSV = 80 Total nSV = 80 Accuracy = 60% (60/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 42 nu = 0.800000 obj = -1.584501, rho = -0.917677 nSV = 80, nBSV = 80 Total nSV = 80 Accuracy = 60% (60/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 42 nu = 0.800000 obj = -2.234566, rho = -0.881583 nSV = 80, nBSV = 80 Total nSV = 80 Accuracy = 60% (60/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -3.121907, rho = -0.829459 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 44 nu = 0.800000 obj = -4.299511, rho = -0.755450 nSV = 82, nBSV = 79 Total nSV = 82 Accuracy = 60% (60/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 44 nu = 0.800000 obj = -5.789016, rho = -0.648227 nSV = 82, nBSV = 79 Total nSV = 82 Accuracy = 77% (77/100) (classification) Accuracy = 62.1% (621/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -7.508629, rho = -0.494382 nSV = 82, nBSV = 79 Total nSV = 82 Accuracy = 92% (92/100) (classification) Accuracy = 89.8% (898/1000) (classification) * optimization finished, #iter = 44 nu = 0.715061 obj = -9.352933, rho = -0.431840 nSV = 73, nBSV = 70 Total nSV = 73 Accuracy = 97% (97/100) (classification) Accuracy = 94.6% (946/1000) (classification) * optimization finished, #iter = 53 nu = 0.637946 obj = -11.420152, rho = -0.382161 nSV = 66, nBSV = 60 Total nSV = 66 Accuracy = 97% (97/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 46 nu = 0.549898 obj = -13.793546, rho = -0.297636 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 42 nu = 0.462367 obj = -16.417107, rho = -0.253691 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 58 nu = 0.374512 obj = -19.572050, rho = -0.296559 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 81 nu = 0.314222 obj = -23.415275, rho = -0.335494 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 51 nu = 0.264513 obj = -27.930620, rho = -0.261252 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 39 nu = 0.218151 obj = -33.179840, rho = -0.288333 nSV = 24, nBSV = 20 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 68 nu = 0.179388 obj = -39.207722, rho = -0.318796 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 85 nu = 0.148458 obj = -46.546422, rho = -0.327304 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 86 nu = 0.123817 obj = -54.477484, rho = -0.367254 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 68 nu = 0.098324 obj = -63.316242, rho = -0.517274 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -0.874240, rho = -0.935161 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -1.241305, rho = -0.906733 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -1.751941, rho = -0.865275 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -2.450525, rho = -0.806205 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -3.381039, rho = -0.721236 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -4.565670, rho = -0.599011 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 77% (77/100) (classification) Accuracy = 67.3% (673/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -5.951328, rho = -0.423198 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 95% (95/100) (classification) Accuracy = 90.4% (904/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -7.487255, rho = -0.402000 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 97% (97/100) (classification) Accuracy = 94.7% (947/1000) (classification) * optimization finished, #iter = 44 nu = 0.730416 obj = -9.248867, rho = -0.346664 nSV = 75, nBSV = 71 Total nSV = 75 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 48 nu = 0.615028 obj = -11.354249, rho = -0.341366 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 38 nu = 0.532419 obj = -13.936220, rho = -0.312048 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 48 nu = 0.455868 obj = -16.913035, rho = -0.241063 nSV = 51, nBSV = 44 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 36 nu = 0.395366 obj = -20.386389, rho = -0.202053 nSV = 40, nBSV = 36 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 27 nu = 0.323927 obj = -24.326131, rho = -0.240142 nSV = 35, nBSV = 31 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 89 nu = 0.268229 obj = -28.991768, rho = -0.198997 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 57 nu = 0.221128 obj = -34.849359, rho = -0.179731 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.190630 obj = -41.771212, rho = -0.011187 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 55 nu = 0.154737 obj = -49.475308, rho = 0.001552 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 85 nu = 0.127451 obj = -59.419988, rho = -0.042704 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 99 nu = 0.107877 obj = -71.152717, rho = -0.031590 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -0.897000, rho = 0.885054 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -1.275784, rho = 0.834656 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -1.805136, rho = 0.762161 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -2.534493, rho = 0.657881 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -3.517239, rho = 0.507878 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -4.793481, rho = 0.292108 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 64% (64/100) (classification) Accuracy = 62.1% (621/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -6.345019, rho = -0.018267 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 89% (89/100) (classification) Accuracy = 88% (880/1000) (classification) * optimization finished, #iter = 44 nu = 0.867588 obj = -8.090647, rho = -0.174205 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 95% (95/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 44 nu = 0.785024 obj = -10.057786, rho = -0.213549 nSV = 80, nBSV = 76 Total nSV = 80 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.673383 obj = -12.322702, rho = -0.185089 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 35 nu = 0.581027 obj = -15.066303, rho = -0.262889 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 38 nu = 0.504260 obj = -18.149861, rho = -0.259840 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 64 nu = 0.419040 obj = -21.493750, rho = -0.215053 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 64 nu = 0.344229 obj = -25.547661, rho = -0.280840 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 37 nu = 0.289266 obj = -30.329458, rho = -0.310089 nSV = 31, nBSV = 28 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.244015 obj = -35.310493, rho = -0.307216 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 68 nu = 0.193465 obj = -40.741649, rho = -0.390747 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 118 nu = 0.153845 obj = -47.592035, rho = -0.322663 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 49 nu = 0.124358 obj = -56.142492, rho = -0.274675 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 43 nu = 0.112763 obj = -63.985481, rho = -0.140317 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -0.914370, rho = -0.918737 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 52.2% (522/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.299111, rho = -0.883107 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 52.2% (522/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.835260, rho = -0.831856 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 52.2% (522/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -2.570724, rho = -0.758133 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 52.2% (522/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -3.554662, rho = -0.652086 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 52.2% (522/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.816912, rho = -0.499543 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 72% (72/100) (classification) Accuracy = 69.3% (693/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.315820, rho = -0.280118 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 97% (97/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 45 nu = 0.869076 obj = -7.955538, rho = -0.201014 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 44 nu = 0.779665 obj = -9.772848, rho = -0.148651 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 44 nu = 0.680000 obj = -11.853046, rho = -0.066412 nSV = 70, nBSV = 65 Total nSV = 70 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 53 nu = 0.559039 obj = -14.181061, rho = -0.027062 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 41 nu = 0.465397 obj = -17.014928, rho = 0.044692 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 79 nu = 0.391398 obj = -20.356119, rho = 0.058505 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 61 nu = 0.325255 obj = -24.253129, rho = 0.084407 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 49 nu = 0.275021 obj = -28.917257, rho = 0.142733 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 53 nu = 0.225706 obj = -34.247350, rho = 0.102759 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 83 nu = 0.185659 obj = -40.198080, rho = 0.256933 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 50 nu = 0.156488 obj = -47.056459, rho = 0.184187 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 86 nu = 0.124449 obj = -54.456531, rho = 0.160255 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 154 nu = 0.103585 obj = -61.701940, rho = 0.147083 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -0.951289, rho = 0.842926 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.350275, rho = 0.774057 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.904836, rho = 0.674993 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -2.662487, rho = 0.532493 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -3.669446, rho = 0.327515 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 54% (54/100) (classification) Accuracy = 55.6% (556/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.946407, rho = 0.032664 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 83% (83/100) (classification) Accuracy = 82.3% (823/1000) (classification) * optimization finished, #iter = 48 nu = 0.951637 obj = -6.446000, rho = -0.225082 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 93% (93/100) (classification) Accuracy = 93.7% (937/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -8.218495, rho = -0.247278 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 94% (94/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 49 nu = 0.792812 obj = -10.276024, rho = -0.162610 nSV = 81, nBSV = 77 Total nSV = 81 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.694018 obj = -12.653943, rho = -0.223971 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 43 nu = 0.597171 obj = -15.379212, rho = -0.146945 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.501458 obj = -18.639964, rho = -0.150360 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 40 nu = 0.422607 obj = -22.713377, rho = -0.114722 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.359916 obj = -27.342722, rho = -0.053940 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 58 nu = 0.301924 obj = -33.135561, rho = -0.055611 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 73 nu = 0.257003 obj = -39.718838, rho = -0.044333 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.212462 obj = -47.550392, rho = 0.016840 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.175973 obj = -56.858402, rho = 0.046052 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.143131 obj = -68.914778, rho = 0.057679 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 50 nu = 0.125430 obj = -83.798601, rho = 0.038539 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -0.896344, rho = -0.934518 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.274426, rho = -0.905807 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.802328, rho = -0.864509 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -2.528682, rho = -0.805103 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -3.505214, rho = -0.719650 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 48.4% (484/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -4.768602, rho = -0.596731 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 67% (67/100) (classification) Accuracy = 59.2% (592/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -6.293540, rho = -0.419917 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 89% (89/100) (classification) Accuracy = 85% (850/1000) (classification) * optimization finished, #iter = 50 nu = 0.855886 obj = -7.987372, rho = -0.319838 nSV = 87, nBSV = 83 Total nSV = 87 Accuracy = 96% (96/100) (classification) Accuracy = 91.9% (919/1000) (classification) * optimization finished, #iter = 44 nu = 0.760000 obj = -10.005788, rho = -0.264245 nSV = 77, nBSV = 74 Total nSV = 77 Accuracy = 98% (98/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 40 nu = 0.671181 obj = -12.359133, rho = -0.184961 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 98% (98/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 42 nu = 0.586279 obj = -15.098168, rho = -0.136589 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 40 nu = 0.483822 obj = -18.414070, rho = -0.130331 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 38 nu = 0.407368 obj = -22.662354, rho = -0.100246 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 22 nu = 0.357706 obj = -27.860689, rho = -0.060179 nSV = 38, nBSV = 34 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 64 nu = 0.310773 obj = -33.733112, rho = -0.119038 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 37 nu = 0.266983 obj = -40.054881, rho = -0.041247 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 95 nu = 0.215289 obj = -47.147160, rho = -0.062826 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 75 nu = 0.174549 obj = -56.269124, rho = -0.052730 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *..* optimization finished, #iter = 202 nu = 0.144084 obj = -67.858904, rho = 0.069382 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 82 nu = 0.119212 obj = -83.003763, rho = 0.024935 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -0.896747, rho = 0.881306 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -1.275260, rho = 0.829265 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -1.804055, rho = 0.755319 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -2.532256, rho = 0.648038 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -3.512609, rho = 0.493721 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -4.783902, rho = 0.271743 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 73% (73/100) (classification) Accuracy = 67.1% (671/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -6.325199, rho = -0.047562 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 92% (92/100) (classification) Accuracy = 89.8% (898/1000) (classification) * optimization finished, #iter = 54 nu = 0.856978 obj = -8.093694, rho = -0.113519 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 95% (95/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 45 nu = 0.776412 obj = -10.155654, rho = -0.138969 nSV = 79, nBSV = 75 Total nSV = 79 Accuracy = 96% (96/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 36 nu = 0.678274 obj = -12.608872, rho = -0.109657 nSV = 68, nBSV = 66 Total nSV = 68 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 51 nu = 0.590071 obj = -15.519900, rho = -0.036339 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 46 nu = 0.505790 obj = -18.795891, rho = -0.083540 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 96% (96/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 92 nu = 0.421517 obj = -22.872569, rho = -0.077520 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 96% (96/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 47 nu = 0.359618 obj = -28.002113, rho = -0.116675 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 96% (96/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.310623 obj = -33.908054, rho = -0.169958 nSV = 35, nBSV = 26 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 72 nu = 0.257460 obj = -40.766237, rho = -0.212895 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.221359 obj = -48.700853, rho = -0.184698 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.178629 obj = -57.984559, rho = -0.177519 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 92 nu = 0.148386 obj = -70.514873, rho = -0.135122 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) ..* optimization finished, #iter = 229 nu = 0.129524 obj = -83.711479, rho = -0.092493 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.951226, rho = -0.904730 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.350144, rho = -0.862960 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.904565, rho = -0.802874 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.661927, rho = -0.716444 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.668287, rho = -0.592119 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 58% (58/100) (classification) Accuracy = 55.8% (558/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.944011, rho = -0.413284 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 90% (90/100) (classification) Accuracy = 84.8% (848/1000) (classification) * optimization finished, #iter = 51 nu = 0.950959 obj = -6.442380, rho = -0.265730 nSV = 97, nBSV = 94 Total nSV = 97 Accuracy = 97% (97/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 45 nu = 0.865667 obj = -8.221322, rho = -0.254980 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 97% (97/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 44 nu = 0.774368 obj = -10.369561, rho = -0.188438 nSV = 80, nBSV = 76 Total nSV = 80 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 48 nu = 0.689182 obj = -12.951666, rho = -0.139559 nSV = 72, nBSV = 66 Total nSV = 72 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 34 nu = 0.601499 obj = -15.994403, rho = -0.179380 nSV = 63, nBSV = 60 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 34 nu = 0.533827 obj = -19.392204, rho = -0.198296 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 42 nu = 0.445572 obj = -23.141394, rho = -0.187304 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 38 nu = 0.372384 obj = -27.494071, rho = -0.220810 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 56 nu = 0.312261 obj = -32.572814, rho = -0.166668 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.255475 obj = -37.935088, rho = -0.144142 nSV = 30, nBSV = 21 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 61 nu = 0.209029 obj = -44.320557, rho = -0.102096 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 194 nu = 0.170441 obj = -51.372480, rho = -0.106546 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.137470 obj = -59.703293, rho = -0.116062 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 232 nu = 0.109559 obj = -68.706711, rho = -0.138247 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.954229, rho = -0.918962 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.356358, rho = -0.883431 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.917422, rho = -0.832322 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.688530, rho = -0.758804 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.723332, rho = -0.653051 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 52% (52/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -5.057907, rho = -0.500931 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 79% (79/100) (classification) Accuracy = 79% (790/1000) (classification) * optimization finished, #iter = 49 nu = 0.969151 obj = -6.660939, rho = -0.299678 nSV = 98, nBSV = 96 Total nSV = 98 Accuracy = 92% (92/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 55 nu = 0.904026 obj = -8.524274, rho = -0.236990 nSV = 92, nBSV = 88 Total nSV = 92 Accuracy = 94% (94/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 47 nu = 0.820000 obj = -10.679514, rho = -0.149400 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 41 nu = 0.709423 obj = -13.216838, rho = -0.098792 nSV = 73, nBSV = 70 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 52 nu = 0.621813 obj = -16.152582, rho = -0.086414 nSV = 65, nBSV = 59 Total nSV = 65 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 35 nu = 0.520365 obj = -19.739117, rho = -0.076144 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 32 nu = 0.442805 obj = -24.122996, rho = -0.059413 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 38 nu = 0.379303 obj = -29.608744, rho = -0.082659 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 48 nu = 0.323837 obj = -36.340126, rho = -0.067692 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 49 nu = 0.280000 obj = -44.252608, rho = 0.015317 nSV = 31, nBSV = 27 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 48 nu = 0.231318 obj = -53.735898, rho = -0.017816 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.197011 obj = -65.486153, rho = 0.054016 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 88 nu = 0.169157 obj = -79.738241, rho = 0.157636 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 125 nu = 0.146938 obj = -95.734274, rho = 0.304086 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 47 nu = 0.860000 obj = -0.839953, rho = 0.922676 nSV = 88, nBSV = 84 Total nSV = 88 Accuracy = 57% (57/100) (classification) Accuracy = 47.3% (473/1000) (classification) * optimization finished, #iter = 47 nu = 0.860000 obj = -1.195587, rho = 0.888774 nSV = 88, nBSV = 84 Total nSV = 88 Accuracy = 57% (57/100) (classification) Accuracy = 47.3% (473/1000) (classification) * optimization finished, #iter = 48 nu = 0.860000 obj = -1.693633, rho = 0.840543 nSV = 88, nBSV = 84 Total nSV = 88 Accuracy = 57% (57/100) (classification) Accuracy = 47.3% (473/1000) (classification) * optimization finished, #iter = 50 nu = 0.860000 obj = -2.382084, rho = 0.770421 nSV = 88, nBSV = 84 Total nSV = 88 Accuracy = 57% (57/100) (classification) Accuracy = 47.3% (473/1000) (classification) * optimization finished, #iter = 52 nu = 0.860000 obj = -3.314519, rho = 0.669782 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 57% (57/100) (classification) Accuracy = 47.3% (473/1000) (classification) * optimization finished, #iter = 52 nu = 0.860000 obj = -4.536037, rho = 0.524998 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 58% (58/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 52 nu = 0.860000 obj = -6.045378, rho = 0.317325 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 77% (77/100) (classification) Accuracy = 73.7% (737/1000) (classification) * optimization finished, #iter = 49 nu = 0.840000 obj = -7.712491, rho = 0.063028 nSV = 86, nBSV = 82 Total nSV = 86 Accuracy = 94% (94/100) (classification) Accuracy = 93.2% (932/1000) (classification) * optimization finished, #iter = 42 nu = 0.737534 obj = -9.543550, rho = 0.031758 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 98% (98/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 39 nu = 0.642570 obj = -11.686600, rho = -0.012589 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 51 nu = 0.555717 obj = -14.227390, rho = 0.005756 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 49 nu = 0.466424 obj = -17.088294, rho = -0.037139 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 80 nu = 0.404800 obj = -20.283679, rho = -0.193976 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 51 nu = 0.322040 obj = -23.890357, rho = -0.219624 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 45 nu = 0.265679 obj = -28.326993, rho = -0.147590 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 88 nu = 0.226614 obj = -33.473641, rho = -0.135774 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 55 nu = 0.184932 obj = -39.188460, rho = -0.076940 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 68 nu = 0.152121 obj = -45.043894, rho = -0.079975 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 142 nu = 0.121342 obj = -50.955906, rho = -0.108023 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.095014 obj = -57.946328, rho = -0.146339 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -0.925886, rho = 0.793748 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.310325, rho = 0.703317 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.840319, rho = 0.573236 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.555092, rho = 0.386122 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.484774, rho = 0.116967 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 73% (73/100) (classification) Accuracy = 66.6% (666/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.618300, rho = -0.270198 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 88% (88/100) (classification) Accuracy = 86.6% (866/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -5.933115, rho = -0.355254 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 92% (92/100) (classification) Accuracy = 88.9% (889/1000) (classification) * optimization finished, #iter = 43 nu = 0.808648 obj = -7.493431, rho = -0.364928 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 96% (96/100) (classification) Accuracy = 93.2% (932/1000) (classification) * optimization finished, #iter = 42 nu = 0.704442 obj = -9.395181, rho = -0.327072 nSV = 73, nBSV = 69 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 94.1% (941/1000) (classification) * optimization finished, #iter = 44 nu = 0.629834 obj = -11.705655, rho = -0.321291 nSV = 65, nBSV = 60 Total nSV = 65 Accuracy = 98% (98/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 39 nu = 0.550145 obj = -14.431327, rho = -0.330356 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 36 nu = 0.485202 obj = -17.539002, rho = -0.292377 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 56 nu = 0.412168 obj = -20.856596, rho = -0.233861 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 52 nu = 0.345345 obj = -24.288038, rho = -0.197256 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.278998 obj = -27.844438, rho = -0.126659 nSV = 34, nBSV = 23 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 66 nu = 0.224852 obj = -31.927184, rho = -0.115241 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 266 nu = 0.179219 obj = -36.097031, rho = -0.226269 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 179 nu = 0.144098 obj = -40.229596, rho = -0.299066 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 234 nu = 0.111229 obj = -44.510097, rho = -0.360754 nSV = 18, nBSV = 6 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 52 nu = 0.085842 obj = -49.099547, rho = -0.408957 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -0.947680, rho = 0.825920 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -1.342806, rho = 0.749595 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -1.889381, rho = 0.639805 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -2.630509, rho = 0.481877 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -3.603280, rho = 0.254706 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 60% (60/100) (classification) Accuracy = 62.7% (627/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -4.809502, rho = -0.072068 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 90% (90/100) (classification) Accuracy = 88.8% (888/1000) (classification) * optimization finished, #iter = 48 nu = 0.945351 obj = -6.174702, rho = -0.271670 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 95% (95/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 56 nu = 0.853008 obj = -7.731459, rho = -0.231867 nSV = 88, nBSV = 83 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 44 nu = 0.750597 obj = -9.533596, rho = -0.174481 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 46 nu = 0.659295 obj = -11.554886, rho = -0.142928 nSV = 69, nBSV = 63 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 40 nu = 0.545092 obj = -13.849502, rho = -0.147890 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 41 nu = 0.458811 obj = -16.509525, rho = -0.199764 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 57 nu = 0.376532 obj = -19.712341, rho = -0.179980 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 41 nu = 0.309071 obj = -23.706429, rho = -0.147436 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 85 nu = 0.263988 obj = -28.471021, rho = -0.161116 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.219026 obj = -33.854568, rho = -0.139837 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 70 nu = 0.177634 obj = -40.825461, rho = -0.145029 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 72 nu = 0.151478 obj = -49.511458, rho = -0.151070 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.127614 obj = -60.113458, rho = -0.151651 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 87 nu = 0.105974 obj = -73.329959, rho = -0.154140 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -0.837805, rho = 0.878628 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 52.6% (526/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -1.191142, rho = 0.825412 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 52.6% (526/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -1.684433, rho = 0.748864 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 52.6% (526/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -2.363041, rho = 0.638753 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 52.6% (526/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -3.275109, rho = 0.480365 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 52.6% (526/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -4.454493, rho = 0.252531 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 71% (71/100) (classification) Accuracy = 65.2% (652/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -5.876651, rho = -0.075197 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 93% (93/100) (classification) Accuracy = 85.7% (857/1000) (classification) * optimization finished, #iter = 40 nu = 0.800000 obj = -7.509672, rho = -0.196439 nSV = 80, nBSV = 80 Total nSV = 80 Accuracy = 96% (96/100) (classification) Accuracy = 91.1% (911/1000) (classification) * optimization finished, #iter = 45 nu = 0.726078 obj = -9.315600, rho = -0.255407 nSV = 75, nBSV = 71 Total nSV = 75 Accuracy = 99% (99/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 32 nu = 0.640000 obj = -11.370373, rho = -0.223061 nSV = 64, nBSV = 64 Total nSV = 64 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 35 nu = 0.547059 obj = -13.563816, rho = -0.212819 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 33 nu = 0.459919 obj = -15.874399, rho = -0.276340 nSV = 48, nBSV = 44 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 30 nu = 0.380000 obj = -18.353478, rho = -0.274304 nSV = 40, nBSV = 37 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.304227 obj = -20.818752, rho = -0.306949 nSV = 35, nBSV = 26 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 37 nu = 0.241119 obj = -23.569689, rho = -0.333743 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 63 nu = 0.198612 obj = -26.278318, rho = -0.335238 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 147 nu = 0.150555 obj = -28.427429, rho = -0.362723 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.113088 obj = -30.983959, rho = -0.346373 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 124 nu = 0.085051 obj = -33.825644, rho = -0.456577 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 169 nu = 0.066361 obj = -37.045869, rho = -0.544129 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -0.804467, rho = -0.941459 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -1.147390, rho = -0.915791 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -1.630193, rho = -0.878870 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -2.303011, rho = -0.825760 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -3.225985, rho = -0.749365 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -4.460855, rho = -0.639474 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -6.045178, rho = -0.481401 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 74% (74/100) (classification) Accuracy = 65.1% (651/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -7.926921, rho = -0.254021 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 93% (93/100) (classification) Accuracy = 91.5% (915/1000) (classification) * optimization finished, #iter = 46 nu = 0.766628 obj = -9.928417, rho = -0.092213 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 97% (97/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 40 nu = 0.678557 obj = -12.164978, rho = -0.147066 nSV = 69, nBSV = 66 Total nSV = 69 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 39 nu = 0.569977 obj = -14.776576, rho = -0.095267 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 68 nu = 0.487681 obj = -17.845784, rho = -0.074233 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 55 nu = 0.398919 obj = -21.594679, rho = -0.065312 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 32 nu = 0.333072 obj = -26.535998, rho = -0.002704 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 29 nu = 0.286489 obj = -32.685440, rho = -0.021809 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 36 nu = 0.245930 obj = -40.320095, rho = -0.060264 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 38 nu = 0.210728 obj = -49.631780, rho = -0.110782 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.181378 obj = -61.463689, rho = -0.020880 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.161155 obj = -74.722472, rho = 0.027935 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.134637 obj = -90.035366, rho = 0.134763 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.840000 obj = -0.822159, rho = 0.926966 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 46 nu = 0.840000 obj = -1.171383, rho = 0.895689 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 58% (58/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 46 nu = 0.840000 obj = -1.661694, rho = 0.849954 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 58% (58/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 47 nu = 0.840000 obj = -2.342094, rho = 0.785894 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 58% (58/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 47 nu = 0.840000 obj = -3.269309, rho = 0.692020 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 58% (58/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 48 nu = 0.840000 obj = -4.496497, rho = 0.556568 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 58% (58/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 48 nu = 0.840000 obj = -6.041244, rho = 0.362145 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 74% (74/100) (classification) Accuracy = 68.6% (686/1000) (classification) * optimization finished, #iter = 51 nu = 0.830004 obj = -7.809266, rho = 0.120682 nSV = 85, nBSV = 80 Total nSV = 85 Accuracy = 96% (96/100) (classification) Accuracy = 91.9% (919/1000) (classification) * optimization finished, #iter = 58 nu = 0.765706 obj = -9.663952, rho = 0.023215 nSV = 79, nBSV = 74 Total nSV = 79 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 49 nu = 0.653594 obj = -11.751417, rho = 0.019530 nSV = 68, nBSV = 64 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 50 nu = 0.561336 obj = -14.120758, rho = 0.072829 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 62 nu = 0.469805 obj = -16.867423, rho = 0.005810 nSV = 51, nBSV = 43 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 63 nu = 0.389037 obj = -20.136575, rho = 0.041753 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 58 nu = 0.324907 obj = -23.941001, rho = -0.021442 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 57 nu = 0.268947 obj = -28.339830, rho = 0.047834 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 66 nu = 0.216624 obj = -33.667007, rho = 0.022947 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 75 nu = 0.180191 obj = -40.314533, rho = 0.096286 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 86 nu = 0.149622 obj = -48.581635, rho = 0.192744 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *..* optimization finished, #iter = 226 nu = 0.126695 obj = -58.203874, rho = 0.164333 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 78 nu = 0.105003 obj = -69.573278, rho = 0.014769 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.945415, rho = -0.903727 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.338120, rho = -0.861516 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.879686, rho = -0.800798 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.610448, rho = -0.713458 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.561771, rho = -0.587824 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 63% (63/100) (classification) Accuracy = 60.1% (601/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.723615, rho = -0.407106 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 92% (92/100) (classification) Accuracy = 84.9% (849/1000) (classification) * optimization finished, #iter = 50 nu = 0.926660 obj = -6.020915, rho = -0.300497 nSV = 95, nBSV = 92 Total nSV = 95 Accuracy = 98% (98/100) (classification) Accuracy = 94.3% (943/1000) (classification) * optimization finished, #iter = 43 nu = 0.827399 obj = -7.526309, rho = -0.340925 nSV = 84, nBSV = 82 Total nSV = 84 Accuracy = 98% (98/100) (classification) Accuracy = 94.8% (948/1000) (classification) * optimization finished, #iter = 44 nu = 0.737639 obj = -9.256615, rho = -0.261637 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 36 nu = 0.623546 obj = -11.267618, rho = -0.280257 nSV = 64, nBSV = 61 Total nSV = 64 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 44 nu = 0.540000 obj = -13.691828, rho = -0.293487 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 38 nu = 0.449101 obj = -16.564422, rho = -0.265718 nSV = 47, nBSV = 43 Total nSV = 47 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 48 nu = 0.374058 obj = -20.006273, rho = -0.287019 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 34 nu = 0.318272 obj = -24.349308, rho = -0.288918 nSV = 33, nBSV = 29 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 37 nu = 0.269198 obj = -29.502946, rho = -0.274803 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 67 nu = 0.225270 obj = -35.456047, rho = -0.303575 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 88 nu = 0.182709 obj = -43.242017, rho = -0.339854 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 63 nu = 0.154823 obj = -53.420218, rho = -0.405269 nSV = 18, nBSV = 13 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 152 nu = 0.138028 obj = -65.265906, rho = -0.496629 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 159 nu = 0.113012 obj = -79.643929, rho = -0.553641 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -0.946310, rho = 0.837151 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.339973, rho = 0.765750 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.883519, rho = 0.663043 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.618380, rho = 0.515305 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 48.5% (485/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.578184, rho = 0.302790 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 66% (66/100) (classification) Accuracy = 62.2% (622/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.757574, rho = -0.002901 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 97% (97/100) (classification) Accuracy = 92.4% (924/1000) (classification) * optimization finished, #iter = 50 nu = 0.923249 obj = -6.086091, rho = -0.120137 nSV = 95, nBSV = 92 Total nSV = 95 Accuracy = 99% (99/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 49 nu = 0.840000 obj = -7.655315, rho = -0.141317 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 40 nu = 0.740000 obj = -9.493188, rho = -0.171007 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 44 nu = 0.650577 obj = -11.577622, rho = -0.071383 nSV = 67, nBSV = 62 Total nSV = 67 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 35 nu = 0.560934 obj = -13.882364, rho = -0.079068 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 44 nu = 0.465820 obj = -16.319680, rho = -0.029418 nSV = 51, nBSV = 44 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 33 nu = 0.388959 obj = -19.062526, rho = -0.010286 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 54 nu = 0.317596 obj = -21.848948, rho = 0.000086 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.253543 obj = -24.898899, rho = 0.012584 nSV = 30, nBSV = 20 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 72 nu = 0.198385 obj = -28.381006, rho = -0.019751 nSV = 25, nBSV = 15 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 77 nu = 0.159897 obj = -32.270621, rho = -0.028311 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.125090 obj = -36.514446, rho = 0.018504 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.098335 obj = -41.222098, rho = 0.011599 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 69 nu = 0.079830 obj = -45.956616, rho = 0.013062 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -0.943727, rho = 0.800533 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.334626, rho = 0.713077 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.872457, rho = 0.587276 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 49.1% (491/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -2.595490, rho = 0.406317 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -3.530821, rho = 0.146016 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 76% (76/100) (classification) Accuracy = 71.4% (714/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -4.659574, rho = -0.228413 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 97% (97/100) (classification) Accuracy = 92.9% (929/1000) (classification) * optimization finished, #iter = 48 nu = 0.914442 obj = -5.941515, rho = -0.312157 nSV = 93, nBSV = 90 Total nSV = 93 Accuracy = 96% (96/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 44 nu = 0.813758 obj = -7.458128, rho = -0.325787 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 97% (97/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 41 nu = 0.720000 obj = -9.244618, rho = -0.280620 nSV = 73, nBSV = 70 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 43 nu = 0.621999 obj = -11.339248, rho = -0.239469 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 48 nu = 0.531691 obj = -13.769665, rho = -0.190655 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 32 nu = 0.449813 obj = -16.755658, rho = -0.201330 nSV = 46, nBSV = 44 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 44 nu = 0.391933 obj = -20.045648, rho = -0.068258 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 61 nu = 0.316576 obj = -23.960409, rho = -0.092009 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 52 nu = 0.268329 obj = -28.591086, rho = -0.104587 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 62 nu = 0.220685 obj = -34.083835, rho = -0.048515 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 37 nu = 0.179711 obj = -40.952675, rho = -0.046650 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 67 nu = 0.153444 obj = -48.867271, rho = -0.077656 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 71 nu = 0.126863 obj = -58.668706, rho = -0.032082 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 162 nu = 0.110601 obj = -69.227006, rho = -0.045594 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.840000 obj = -0.819826, rho = -0.947792 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 45 nu = 0.840000 obj = -1.166557, rho = -0.925260 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 44 nu = 0.840000 obj = -1.651708, rho = -0.892490 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 44 nu = 0.840000 obj = -2.321427, rho = -0.845353 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -3.226548, rho = -0.777548 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -4.408017, rho = -0.680013 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 61% (61/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -5.858168, rho = -0.539715 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 82% (82/100) (classification) Accuracy = 76.2% (762/1000) (classification) * optimization finished, #iter = 41 nu = 0.800000 obj = -7.471871, rho = -0.428598 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 97% (97/100) (classification) Accuracy = 91.6% (916/1000) (classification) * optimization finished, #iter = 41 nu = 0.731559 obj = -9.253798, rho = -0.412884 nSV = 74, nBSV = 71 Total nSV = 74 Accuracy = 98% (98/100) (classification) Accuracy = 94.7% (947/1000) (classification) * optimization finished, #iter = 35 nu = 0.639660 obj = -11.110873, rho = -0.296555 nSV = 65, nBSV = 62 Total nSV = 65 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 35 nu = 0.538915 obj = -13.207340, rho = -0.245707 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 39 nu = 0.443778 obj = -15.497518, rho = -0.241360 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 82 nu = 0.363643 obj = -18.056173, rho = -0.171420 nSV = 38, nBSV = 33 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 79 nu = 0.292084 obj = -21.144301, rho = -0.161407 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 71 nu = 0.239819 obj = -24.562440, rho = -0.183269 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 130 nu = 0.196120 obj = -28.501230, rho = -0.160459 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.159847 obj = -32.431277, rho = -0.157527 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 94 nu = 0.127794 obj = -36.726409, rho = -0.134092 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 87 nu = 0.102623 obj = -40.537545, rho = -0.151417 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 191 nu = 0.081482 obj = -43.137068, rho = -0.127889 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.954418, rho = 0.860760 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.356749, rho = 0.799711 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.918231, rho = 0.711894 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.690204, rho = 0.585574 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.726797, rho = 0.403869 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 53% (53/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -5.065075, rho = 0.142495 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 75% (75/100) (classification) Accuracy = 77.1% (771/1000) (classification) * optimization finished, #iter = 49 nu = 0.966182 obj = -6.677066, rho = -0.134124 nSV = 98, nBSV = 96 Total nSV = 98 Accuracy = 92% (92/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 48 nu = 0.910335 obj = -8.567563, rho = -0.237138 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 95% (95/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 46 nu = 0.820000 obj = -10.763448, rho = -0.158067 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 38 nu = 0.720000 obj = -13.318520, rho = -0.139616 nSV = 73, nBSV = 71 Total nSV = 73 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 38 nu = 0.630419 obj = -16.321243, rho = -0.084261 nSV = 64, nBSV = 61 Total nSV = 64 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 59 nu = 0.525565 obj = -19.840070, rho = -0.113985 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 35 nu = 0.446291 obj = -24.235853, rho = -0.163980 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.385192 obj = -29.429120, rho = -0.258800 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 53 nu = 0.323254 obj = -35.524872, rho = -0.117760 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 66 nu = 0.266712 obj = -43.193676, rho = -0.119612 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 138 nu = 0.233996 obj = -52.341515, rho = -0.235923 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 136 nu = 0.196746 obj = -62.797531, rho = -0.298941 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.160004 obj = -75.698331, rho = -0.295791 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) ..* optimization finished, #iter = 293 nu = 0.133159 obj = -92.710535, rho = -0.350881 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -0.933138, rho = 0.867357 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.325332, rho = 0.809200 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.871369, rho = 0.725544 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.619339, rho = 0.605209 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.617711, rho = 0.432113 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 54% (54/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.893366, rho = 0.183123 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 72% (72/100) (classification) Accuracy = 74.8% (748/1000) (classification) * optimization finished, #iter = 48 nu = 0.947669 obj = -6.398219, rho = -0.108571 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 95% (95/100) (classification) Accuracy = 93.8% (938/1000) (classification) * optimization finished, #iter = 45 nu = 0.865957 obj = -8.141594, rho = -0.125636 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 48 nu = 0.780322 obj = -10.170390, rho = -0.126013 nSV = 80, nBSV = 76 Total nSV = 80 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 52 nu = 0.692007 obj = -12.456955, rho = -0.167105 nSV = 73, nBSV = 67 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.590470 obj = -15.108711, rho = -0.152542 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 60 nu = 0.504269 obj = -17.995599, rho = -0.198384 nSV = 55, nBSV = 47 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 57 nu = 0.412669 obj = -21.338208, rho = -0.176157 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 47 nu = 0.339544 obj = -25.501136, rho = -0.201593 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 53 nu = 0.280088 obj = -30.632265, rho = -0.193287 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 36 nu = 0.236288 obj = -36.839902, rho = -0.191291 nSV = 26, nBSV = 22 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 49 nu = 0.205645 obj = -43.467870, rho = -0.242047 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 91 nu = 0.169465 obj = -50.363633, rho = -0.381301 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*...* optimization finished, #iter = 496 nu = 0.133367 obj = -57.654860, rho = -0.427025 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 68 nu = 0.105336 obj = -67.440255, rho = -0.436985 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 41 nu = 0.720000 obj = -0.708293, rho = -0.965894 nSV = 74, nBSV = 70 Total nSV = 74 Accuracy = 64% (64/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 41 nu = 0.720000 obj = -1.011461, rho = -0.950941 nSV = 74, nBSV = 70 Total nSV = 74 Accuracy = 64% (64/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 42 nu = 0.720000 obj = -1.439660, rho = -0.928673 nSV = 74, nBSV = 69 Total nSV = 74 Accuracy = 64% (64/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 44 nu = 0.720000 obj = -2.039276, rho = -0.897641 nSV = 74, nBSV = 69 Total nSV = 74 Accuracy = 64% (64/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 44 nu = 0.720000 obj = -2.867997, rho = -0.852762 nSV = 74, nBSV = 69 Total nSV = 74 Accuracy = 64% (64/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 46 nu = 0.720000 obj = -3.990156, rho = -0.788462 nSV = 74, nBSV = 69 Total nSV = 74 Accuracy = 64% (64/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 48 nu = 0.720000 obj = -5.459649, rho = -0.695919 nSV = 74, nBSV = 69 Total nSV = 74 Accuracy = 64% (64/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 40 nu = 0.720000 obj = -7.274090, rho = -0.563342 nSV = 74, nBSV = 70 Total nSV = 74 Accuracy = 84% (84/100) (classification) Accuracy = 72.5% (725/1000) (classification) * optimization finished, #iter = 48 nu = 0.720000 obj = -9.264674, rho = -0.370966 nSV = 74, nBSV = 69 Total nSV = 74 Accuracy = 99% (99/100) (classification) Accuracy = 94% (940/1000) (classification) * optimization finished, #iter = 37 nu = 0.651900 obj = -11.204326, rho = -0.298290 nSV = 67, nBSV = 64 Total nSV = 67 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 46 nu = 0.539706 obj = -13.232930, rho = -0.315636 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.442982 obj = -15.619399, rho = -0.333383 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 34 nu = 0.363310 obj = -18.440438, rho = -0.301819 nSV = 39, nBSV = 35 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 45 nu = 0.296735 obj = -21.730061, rho = -0.322014 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 37 nu = 0.251559 obj = -25.392093, rho = -0.240694 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 64 nu = 0.202010 obj = -29.023010, rho = -0.206482 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 30 nu = 0.163605 obj = -33.053569, rho = -0.146862 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.133709 obj = -36.968549, rho = -0.148029 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 148 nu = 0.104828 obj = -39.759257, rho = -0.119442 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.080610 obj = -41.876126, rho = -0.236675 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -0.873587, rho = 0.878403 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -1.239954, rho = 0.825089 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -1.749143, rho = 0.748399 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -2.444736, rho = 0.638085 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -3.369061, rho = 0.479404 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -4.540886, rho = 0.251148 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 77% (77/100) (classification) Accuracy = 73.2% (732/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -5.900045, rho = -0.077186 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 96% (96/100) (classification) Accuracy = 94.4% (944/1000) (classification) * optimization finished, #iter = 43 nu = 0.805421 obj = -7.409678, rho = -0.104431 nSV = 82, nBSV = 79 Total nSV = 82 Accuracy = 97% (97/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 46 nu = 0.705354 obj = -9.221752, rho = -0.113882 nSV = 73, nBSV = 70 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 33 nu = 0.615424 obj = -11.387801, rho = -0.070434 nSV = 62, nBSV = 60 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 40 nu = 0.537293 obj = -13.867524, rho = -0.128315 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 42 nu = 0.457149 obj = -16.731320, rho = -0.098906 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 34 nu = 0.391789 obj = -20.038694, rho = -0.016099 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 55 nu = 0.324253 obj = -23.708138, rho = -0.001716 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 88 nu = 0.266391 obj = -28.028038, rho = 0.043384 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.217474 obj = -33.032139, rho = 0.056468 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 66 nu = 0.183729 obj = -38.614654, rho = 0.186022 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.146658 obj = -44.724864, rho = 0.214357 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 97 nu = 0.116526 obj = -52.430440, rho = 0.184630 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 60 nu = 0.095583 obj = -62.172781, rho = 0.245535 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -0.911256, rho = -0.947393 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.292667, rho = -0.924327 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.821926, rho = -0.891148 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -2.543135, rho = -0.843422 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -3.497577, rho = -0.774770 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.698796, rho = -0.676018 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 81% (81/100) (classification) Accuracy = 71.4% (714/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.071421, rho = -0.553241 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 96% (96/100) (classification) Accuracy = 91.2% (912/1000) (classification) * optimization finished, #iter = 52 nu = 0.837087 obj = -7.558885, rho = -0.521613 nSV = 85, nBSV = 81 Total nSV = 85 Accuracy = 97% (97/100) (classification) Accuracy = 93.8% (938/1000) (classification) * optimization finished, #iter = 42 nu = 0.730302 obj = -9.314473, rho = -0.473504 nSV = 76, nBSV = 72 Total nSV = 76 Accuracy = 99% (99/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 47 nu = 0.620000 obj = -11.442089, rho = -0.469086 nSV = 64, nBSV = 61 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 41 nu = 0.526757 obj = -14.110506, rho = -0.474252 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 40 nu = 0.461235 obj = -17.348459, rho = -0.440054 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 37 nu = 0.391221 obj = -21.227245, rho = -0.478141 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 28 nu = 0.349948 obj = -25.629887, rho = -0.339904 nSV = 38, nBSV = 33 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 51 nu = 0.292293 obj = -30.290856, rho = -0.427469 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *..* optimization finished, #iter = 247 nu = 0.234902 obj = -35.312692, rho = -0.466621 nSV = 28, nBSV = 18 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 67 nu = 0.195561 obj = -41.751080, rho = -0.457464 nSV = 22, nBSV = 17 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 194 nu = 0.158808 obj = -48.562013, rho = -0.428804 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *..* optimization finished, #iter = 200 nu = 0.127627 obj = -56.953335, rho = -0.480510 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 204 nu = 0.103451 obj = -66.765679, rho = -0.552172 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -0.838459, rho = 0.913247 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 46.9% (469/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -1.192510, rho = 0.875409 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 46.9% (469/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -1.687263, rho = 0.820783 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 46.9% (469/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -2.368897, rho = 0.742205 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 46.9% (469/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -3.287227, rho = 0.629175 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 46.9% (469/1000) (classification) * optimization finished, #iter = 47 nu = 0.860000 obj = -4.479570, rho = 0.466825 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 62% (62/100) (classification) Accuracy = 52.7% (527/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -5.928538, rho = 0.233055 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 87% (87/100) (classification) Accuracy = 83.5% (835/1000) (classification) * optimization finished, #iter = 47 nu = 0.826268 obj = -7.535866, rho = 0.022466 nSV = 84, nBSV = 80 Total nSV = 84 Accuracy = 98% (98/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 42 nu = 0.720000 obj = -9.352920, rho = 0.002637 nSV = 73, nBSV = 71 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 38 nu = 0.629800 obj = -11.470365, rho = -0.086760 nSV = 64, nBSV = 61 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 72 nu = 0.550596 obj = -13.894563, rho = -0.095017 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 40 nu = 0.466744 obj = -16.554624, rho = -0.100028 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 37 nu = 0.388103 obj = -19.491852, rho = -0.037535 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 86 nu = 0.319851 obj = -22.703483, rho = -0.037258 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 187 nu = 0.255458 obj = -26.469296, rho = -0.060900 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.203793 obj = -31.246963, rho = -0.023201 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 90 nu = 0.166744 obj = -37.330831, rho = 0.091577 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 144 nu = 0.140421 obj = -44.225137, rho = 0.073986 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 69 nu = 0.116023 obj = -52.804102, rho = 0.122045 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.096427 obj = -62.548880, rho = 0.138376 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -0.878951, rho = -0.948632 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 47.9% (479/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -1.251051, rho = -0.926109 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 47.9% (479/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -1.772106, rho = -0.893712 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 47.9% (479/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -2.492249, rho = -0.847110 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 47.9% (479/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -3.467373, rho = -0.780075 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 47.9% (479/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -4.744307, rho = -0.683649 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 64% (64/100) (classification) Accuracy = 54.8% (548/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -6.320952, rho = -0.544944 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 86% (86/100) (classification) Accuracy = 83.9% (839/1000) (classification) * optimization finished, #iter = 47 nu = 0.855114 obj = -8.099786, rho = -0.472257 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 95% (95/100) (classification) Accuracy = 94.3% (943/1000) (classification) * optimization finished, #iter = 49 nu = 0.765594 obj = -10.213950, rho = -0.428132 nSV = 78, nBSV = 74 Total nSV = 78 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 38 nu = 0.689644 obj = -12.765242, rho = -0.312794 nSV = 70, nBSV = 68 Total nSV = 70 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 42 nu = 0.601208 obj = -15.554063, rho = -0.312127 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 54 nu = 0.511508 obj = -18.786444, rho = -0.277004 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 46 nu = 0.426556 obj = -22.693601, rho = -0.305190 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 39 nu = 0.364310 obj = -27.375191, rho = -0.287379 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 71 nu = 0.308102 obj = -32.721287, rho = -0.232570 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.248497 obj = -39.152342, rho = -0.217305 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 31 nu = 0.211500 obj = -47.184720, rho = -0.276461 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 35 nu = 0.178780 obj = -56.190290, rho = -0.332849 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 79 nu = 0.150836 obj = -65.754020, rho = -0.481467 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 241 nu = 0.122958 obj = -75.310664, rho = -0.626229 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -0.928598, rho = 0.843119 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 51 nu = 0.960000 obj = -1.315938, rho = 0.773600 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 51 nu = 0.960000 obj = -1.851933, rho = 0.674335 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -2.579124, rho = 0.531548 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 48% (480/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -3.534500, rho = 0.326155 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 60% (60/100) (classification) Accuracy = 54.9% (549/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -4.721191, rho = 0.030707 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 86% (86/100) (classification) Accuracy = 82.5% (825/1000) (classification) * optimization finished, #iter = 48 nu = 0.914358 obj = -6.072955, rho = -0.167170 nSV = 93, nBSV = 90 Total nSV = 93 Accuracy = 96% (96/100) (classification) Accuracy = 93.2% (932/1000) (classification) * optimization finished, #iter = 43 nu = 0.820000 obj = -7.707893, rho = -0.212689 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 37 nu = 0.734371 obj = -9.705788, rho = -0.166369 nSV = 74, nBSV = 72 Total nSV = 74 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 39 nu = 0.658228 obj = -12.014480, rho = -0.236180 nSV = 67, nBSV = 64 Total nSV = 67 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 33 nu = 0.569943 obj = -14.583212, rho = -0.170487 nSV = 59, nBSV = 56 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 56 nu = 0.480740 obj = -17.496941, rho = -0.151775 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 39 nu = 0.405512 obj = -20.835215, rho = -0.184473 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 62 nu = 0.333925 obj = -24.746862, rho = -0.255900 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 41 nu = 0.274024 obj = -29.476783, rho = -0.330461 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 38 nu = 0.227828 obj = -35.305755, rho = -0.384197 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 46 nu = 0.189808 obj = -42.172238, rho = -0.351475 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 59 nu = 0.160551 obj = -50.504818, rho = -0.275640 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 93 nu = 0.134654 obj = -58.646917, rho = -0.294689 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 141 nu = 0.107132 obj = -68.588609, rho = -0.263042 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.952996, rho = -0.924969 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.1% (511/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.353806, rho = -0.892072 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.1% (511/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.912143, rho = -0.844750 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.1% (511/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.677606, rho = -0.776681 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.1% (511/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.700729, rho = -0.678767 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 53% (53/100) (classification) Accuracy = 53.8% (538/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -5.011137, rho = -0.537922 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 86% (86/100) (classification) Accuracy = 78.9% (789/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -6.573143, rho = -0.430160 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 91% (91/100) (classification) Accuracy = 91.7% (917/1000) (classification) * optimization finished, #iter = 48 nu = 0.892644 obj = -8.383071, rho = -0.355334 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 95% (95/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 45 nu = 0.792010 obj = -10.567586, rho = -0.335050 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 97% (97/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 38 nu = 0.696332 obj = -13.252526, rho = -0.313113 nSV = 71, nBSV = 68 Total nSV = 71 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 43 nu = 0.627271 obj = -16.377358, rho = -0.285764 nSV = 64, nBSV = 61 Total nSV = 64 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.542221 obj = -19.828339, rho = -0.324247 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.464427 obj = -23.845018, rho = -0.249543 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 66 nu = 0.381441 obj = -28.184132, rho = -0.233417 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 63 nu = 0.318031 obj = -33.239419, rho = -0.341744 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.259056 obj = -39.331527, rho = -0.300459 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 49 nu = 0.213431 obj = -46.525841, rho = -0.339142 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 215 nu = 0.177747 obj = -54.652795, rho = -0.332312 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 54 nu = 0.147236 obj = -63.801755, rho = -0.315773 nSV = 17, nBSV = 12 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 122 nu = 0.122096 obj = -71.986451, rho = -0.373148 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -0.916195, rho = 0.884680 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -1.302887, rho = 0.834118 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.843073, rho = 0.761387 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.586890, rho = 0.656767 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.588112, rho = 0.506277 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.886125, rho = 0.289804 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 70% (70/100) (classification) Accuracy = 67.4% (674/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.459031, rho = -0.021581 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 92% (92/100) (classification) Accuracy = 93.4% (934/1000) (classification) * optimization finished, #iter = 48 nu = 0.871395 obj = -8.238051, rho = -0.076524 nSV = 89, nBSV = 85 Total nSV = 89 Accuracy = 94% (94/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 40 nu = 0.780000 obj = -10.360410, rho = -0.034009 nSV = 78, nBSV = 78 Total nSV = 78 Accuracy = 95% (95/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 44 nu = 0.696669 obj = -12.861519, rho = -0.047159 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 96% (96/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.600078 obj = -15.761300, rho = -0.093317 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 58 nu = 0.513618 obj = -19.260614, rho = -0.046570 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 53 nu = 0.433432 obj = -23.459860, rho = -0.021131 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 53 nu = 0.370822 obj = -28.491693, rho = -0.045760 nSV = 41, nBSV = 33 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 74 nu = 0.316976 obj = -34.530616, rho = -0.125964 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 67 nu = 0.269897 obj = -41.427391, rho = -0.101567 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.220552 obj = -49.544403, rho = -0.081876 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 65 nu = 0.188712 obj = -59.173092, rho = -0.144839 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 72 nu = 0.160870 obj = -69.668145, rho = -0.224648 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 228 nu = 0.132144 obj = -79.124803, rho = -0.290836 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -0.873640, rho = 0.864814 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -1.240062, rho = 0.805542 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -1.749367, rho = 0.720282 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -2.445200, rho = 0.597640 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -3.370021, rho = 0.421225 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 56% (56/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -4.542872, rho = 0.167461 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 82% (82/100) (classification) Accuracy = 75.9% (759/1000) (classification) * optimization finished, #iter = 45 nu = 0.882120 obj = -5.909163, rho = -0.114436 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 94% (94/100) (classification) Accuracy = 92% (920/1000) (classification) * optimization finished, #iter = 41 nu = 0.812734 obj = -7.442513, rho = -0.182487 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 96% (96/100) (classification) Accuracy = 94.4% (944/1000) (classification) * optimization finished, #iter = 42 nu = 0.722882 obj = -9.187483, rho = -0.175404 nSV = 75, nBSV = 70 Total nSV = 75 Accuracy = 97% (97/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 54 nu = 0.629718 obj = -11.218721, rho = -0.156001 nSV = 65, nBSV = 60 Total nSV = 65 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 41 nu = 0.520123 obj = -13.642547, rho = -0.166694 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 41 nu = 0.441961 obj = -16.718526, rho = -0.178321 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 42 nu = 0.371457 obj = -20.506483, rho = -0.163243 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 71 nu = 0.321184 obj = -25.219313, rho = -0.200197 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 58 nu = 0.268898 obj = -31.162654, rho = -0.262297 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 44 nu = 0.237612 obj = -38.599462, rho = -0.303440 nSV = 27, nBSV = 22 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.208538 obj = -46.527895, rho = -0.305336 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 192 nu = 0.168238 obj = -56.392257, rho = -0.271422 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 153 nu = 0.142947 obj = -68.781075, rho = -0.162189 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 134 nu = 0.118946 obj = -84.944483, rho = -0.135601 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -0.929767, rho = 0.840535 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.318355, rho = 0.770618 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.856935, rho = 0.670045 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.589473, rho = 0.525376 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.555913, rho = 0.317277 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 58% (58/100) (classification) Accuracy = 54.5% (545/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.765496, rho = 0.017938 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 80% (80/100) (classification) Accuracy = 79.8% (798/1000) (classification) * optimization finished, #iter = 49 nu = 0.936042 obj = -6.149934, rho = -0.263106 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 98% (98/100) (classification) Accuracy = 92.6% (926/1000) (classification) * optimization finished, #iter = 48 nu = 0.847331 obj = -7.760038, rho = -0.283619 nSV = 86, nBSV = 82 Total nSV = 86 Accuracy = 99% (99/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 44 nu = 0.752623 obj = -9.653834, rho = -0.279518 nSV = 77, nBSV = 74 Total nSV = 77 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 50 nu = 0.664886 obj = -11.801124, rho = -0.167537 nSV = 69, nBSV = 64 Total nSV = 69 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 33 nu = 0.564045 obj = -14.236589, rho = -0.189204 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.480000 obj = -16.849283, rho = -0.150303 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 38 nu = 0.393678 obj = -19.762254, rho = -0.198687 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 68 nu = 0.317115 obj = -23.165679, rho = -0.177688 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 35 nu = 0.253311 obj = -27.645334, rho = -0.222501 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 31 nu = 0.215720 obj = -33.060827, rho = -0.265283 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 64 nu = 0.175779 obj = -39.640270, rho = -0.328452 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 83 nu = 0.143603 obj = -48.132459, rho = -0.356941 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.122686 obj = -58.831505, rho = -0.347456 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 73 nu = 0.109986 obj = -69.726816, rho = -0.225507 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -0.927956, rho = -0.917878 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.314609, rho = -0.881872 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.849183, rho = -0.830078 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -2.573433, rho = -0.755576 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -3.522724, rho = -0.648409 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 61% (61/100) (classification) Accuracy = 60.2% (602/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -4.696824, rho = -0.494253 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 86% (86/100) (classification) Accuracy = 83.3% (833/1000) (classification) * optimization finished, #iter = 46 nu = 0.912526 obj = -6.054171, rho = -0.366158 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 94% (94/100) (classification) Accuracy = 91.7% (917/1000) (classification) * optimization finished, #iter = 44 nu = 0.822973 obj = -7.645675, rho = -0.318751 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 95% (95/100) (classification) Accuracy = 94.4% (944/1000) (classification) * optimization finished, #iter = 43 nu = 0.733460 obj = -9.543580, rho = -0.284601 nSV = 75, nBSV = 71 Total nSV = 75 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 44 nu = 0.642602 obj = -11.810679, rho = -0.257958 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 61 nu = 0.554456 obj = -14.415405, rho = -0.193757 nSV = 60, nBSV = 53 Total nSV = 60 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 37 nu = 0.467920 obj = -17.551992, rho = -0.170287 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 34 nu = 0.394673 obj = -21.486842, rho = -0.166937 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 29 nu = 0.335234 obj = -26.292658, rho = -0.141594 nSV = 35, nBSV = 31 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.295304 obj = -32.139694, rho = -0.083846 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.250978 obj = -38.483187, rho = -0.074311 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 60 nu = 0.207192 obj = -45.732273, rho = -0.120712 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 78 nu = 0.176101 obj = -53.913773, rho = -0.031223 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.140798 obj = -63.074449, rho = -0.020461 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 34 nu = 0.124633 obj = -72.277633, rho = -0.186804 nSV = 14, nBSV = 9 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 40 nu = 0.760000 obj = -0.744512, rho = 0.933682 nSV = 77, nBSV = 75 Total nSV = 77 Accuracy = 62% (62/100) (classification) Accuracy = 47.2% (472/1000) (classification) * optimization finished, #iter = 40 nu = 0.760000 obj = -1.061174, rho = 0.904605 nSV = 77, nBSV = 75 Total nSV = 77 Accuracy = 62% (62/100) (classification) Accuracy = 47.2% (472/1000) (classification) * optimization finished, #iter = 40 nu = 0.760000 obj = -1.506234, rho = 0.862779 nSV = 77, nBSV = 75 Total nSV = 77 Accuracy = 62% (62/100) (classification) Accuracy = 47.2% (472/1000) (classification) * optimization finished, #iter = 40 nu = 0.760000 obj = -2.124820, rho = 0.802615 nSV = 77, nBSV = 75 Total nSV = 77 Accuracy = 62% (62/100) (classification) Accuracy = 47.2% (472/1000) (classification) * optimization finished, #iter = 40 nu = 0.760000 obj = -2.969913, rho = 0.716071 nSV = 77, nBSV = 75 Total nSV = 77 Accuracy = 62% (62/100) (classification) Accuracy = 47.2% (472/1000) (classification) * optimization finished, #iter = 40 nu = 0.760000 obj = -4.093019, rho = 0.591583 nSV = 77, nBSV = 75 Total nSV = 77 Accuracy = 62% (62/100) (classification) Accuracy = 47.2% (472/1000) (classification) * optimization finished, #iter = 40 nu = 0.760000 obj = -5.517119, rho = 0.412512 nSV = 77, nBSV = 75 Total nSV = 77 Accuracy = 73% (73/100) (classification) Accuracy = 62.2% (622/1000) (classification) * optimization finished, #iter = 39 nu = 0.760000 obj = -7.169519, rho = 0.154928 nSV = 77, nBSV = 75 Total nSV = 77 Accuracy = 93% (93/100) (classification) Accuracy = 87.9% (879/1000) (classification) * optimization finished, #iter = 41 nu = 0.700000 obj = -8.908775, rho = 0.050846 nSV = 71, nBSV = 68 Total nSV = 71 Accuracy = 99% (99/100) (classification) Accuracy = 95.1% (951/1000) (classification) * optimization finished, #iter = 40 nu = 0.602940 obj = -10.843516, rho = -0.011029 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 34 nu = 0.511496 obj = -13.133903, rho = -0.022084 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 42 nu = 0.442871 obj = -15.649351, rho = -0.091912 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 69 nu = 0.360253 obj = -18.519172, rho = -0.093951 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 37 nu = 0.291875 obj = -22.185024, rho = -0.131407 nSV = 31, nBSV = 27 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 61 nu = 0.239624 obj = -26.887438, rho = -0.052532 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 39 nu = 0.212117 obj = -32.600623, rho = -0.088491 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 49 nu = 0.179080 obj = -38.724630, rho = -0.060509 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.144437 obj = -45.584821, rho = -0.083398 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.120058 obj = -53.652313, rho = -0.261700 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 70 nu = 0.099891 obj = -62.786570, rho = -0.203039 nSV = 12, nBSV = 7 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -0.931510, rho = -0.916579 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.321961, rho = -0.880003 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.864396, rho = -0.827390 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.604911, rho = -0.751710 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.587856, rho = -0.642847 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.831591, rho = -0.486253 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 78% (78/100) (classification) Accuracy = 75.8% (758/1000) (classification) * optimization finished, #iter = 48 nu = 0.952748 obj = -6.269121, rho = -0.287353 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 96% (96/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 45 nu = 0.847967 obj = -7.909378, rho = -0.213797 nSV = 88, nBSV = 83 Total nSV = 88 Accuracy = 96% (96/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 38 nu = 0.760000 obj = -9.930192, rho = -0.189257 nSV = 76, nBSV = 76 Total nSV = 76 Accuracy = 96% (96/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 52 nu = 0.671194 obj = -12.286208, rho = -0.137229 nSV = 69, nBSV = 66 Total nSV = 69 Accuracy = 96% (96/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 50 nu = 0.578699 obj = -15.007402, rho = -0.125600 nSV = 61, nBSV = 53 Total nSV = 61 Accuracy = 96% (96/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 54 nu = 0.491222 obj = -18.270358, rho = -0.068553 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.413902 obj = -22.179163, rho = -0.049316 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 54 nu = 0.353312 obj = -26.910430, rho = -0.035069 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 77 nu = 0.295700 obj = -32.447465, rho = -0.020865 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 79 nu = 0.248207 obj = -39.271601, rho = 0.032479 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 79 nu = 0.208142 obj = -47.387824, rho = 0.028842 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) .*.* optimization finished, #iter = 224 nu = 0.173318 obj = -57.631724, rho = -0.031711 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) .* optimization finished, #iter = 135 nu = 0.151277 obj = -69.739563, rho = -0.134862 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) .*..* optimization finished, #iter = 387 nu = 0.124670 obj = -83.130054, rho = -0.158921 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.951028, rho = -0.882040 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.349734, rho = -0.830320 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.903717, rho = -0.755924 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.660171, rho = -0.648910 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.664655, rho = -0.494974 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 59% (59/100) (classification) Accuracy = 56.7% (567/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.936496, rho = -0.273545 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 88% (88/100) (classification) Accuracy = 89.5% (895/1000) (classification) * optimization finished, #iter = 51 nu = 0.965742 obj = -6.410870, rho = -0.164594 nSV = 98, nBSV = 95 Total nSV = 98 Accuracy = 92% (92/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 49 nu = 0.879247 obj = -8.101915, rho = -0.145418 nSV = 90, nBSV = 86 Total nSV = 90 Accuracy = 91% (91/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 43 nu = 0.779305 obj = -10.097734, rho = -0.102505 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 95% (95/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.677650 obj = -12.440062, rho = -0.091876 nSV = 71, nBSV = 66 Total nSV = 71 Accuracy = 95% (95/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.579263 obj = -15.233166, rho = -0.055648 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 95% (95/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 49 nu = 0.481240 obj = -18.772830, rho = -0.029048 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 95% (95/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 61 nu = 0.419541 obj = -23.272209, rho = -0.052510 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 95% (95/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 50 nu = 0.357961 obj = -28.962700, rho = -0.058338 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 95% (95/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 24 nu = 0.312087 obj = -36.208944, rho = -0.085107 nSV = 33, nBSV = 30 Total nSV = 33 Accuracy = 96% (96/100) (classification) Accuracy = 99.3% (993/1000) (classification) * optimization finished, #iter = 22 nu = 0.267795 obj = -45.080711, rho = -0.091734 nSV = 30, nBSV = 26 Total nSV = 30 Accuracy = 97% (97/100) (classification) Accuracy = 99.5% (995/1000) (classification) * optimization finished, #iter = 47 nu = 0.232882 obj = -56.407575, rho = -0.142222 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 97% (97/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 63 nu = 0.197183 obj = -70.863330, rho = -0.173601 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 99.3% (993/1000) (classification) *.* optimization finished, #iter = 148 nu = 0.175461 obj = -89.883549, rho = -0.277110 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 99.1% (991/1000) (classification) .* optimization finished, #iter = 176 nu = 0.152457 obj = -113.950314, rho = -0.268828 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 97% (97/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.954743, rho = -0.898952 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.4% (494/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.357420, rho = -0.854648 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.4% (494/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.919621, rho = -0.790918 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.4% (494/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.693079, rho = -0.699246 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.4% (494/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.732745, rho = -0.567381 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.4% (494/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -5.077383, rho = -0.377699 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 70% (70/100) (classification) Accuracy = 67.7% (677/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -6.699407, rho = -0.104851 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 93% (93/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 47 nu = 0.913401 obj = -8.517237, rho = -0.048503 nSV = 93, nBSV = 90 Total nSV = 93 Accuracy = 96% (96/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 43 nu = 0.810216 obj = -10.633405, rho = -0.059978 nSV = 82, nBSV = 79 Total nSV = 82 Accuracy = 96% (96/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 52 nu = 0.715119 obj = -13.120129, rho = -0.009916 nSV = 74, nBSV = 68 Total nSV = 74 Accuracy = 96% (96/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 36 nu = 0.620000 obj = -16.068173, rho = 0.111718 nSV = 63, nBSV = 60 Total nSV = 63 Accuracy = 96% (96/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 38 nu = 0.517834 obj = -19.587339, rho = 0.094421 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 58 nu = 0.443404 obj = -23.961795, rho = 0.090418 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 96% (96/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 83 nu = 0.368857 obj = -29.530413, rho = 0.107237 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 53 nu = 0.310909 obj = -36.872945, rho = 0.078465 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 96% (96/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 30 nu = 0.278457 obj = -46.052513, rho = 0.072237 nSV = 29, nBSV = 25 Total nSV = 29 Accuracy = 96% (96/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 98 nu = 0.241515 obj = -57.032843, rho = -0.005711 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 96% (96/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 82 nu = 0.202590 obj = -70.836571, rho = -0.061309 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 50 nu = 0.177037 obj = -88.951638, rho = -0.051334 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 96 nu = 0.150490 obj = -111.839053, rho = -0.044557 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -0.898827, rho = -0.938736 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.279565, rho = -0.911875 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.812960, rho = -0.873237 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -2.550682, rho = -0.817657 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -3.550735, rho = -0.737709 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -4.862790, rho = -0.622708 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 60% (60/100) (classification) Accuracy = 60% (600/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -6.488429, rho = -0.457284 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 86% (86/100) (classification) Accuracy = 87% (870/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -8.341899, rho = -0.320494 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 92% (92/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 42 nu = 0.799775 obj = -10.465995, rho = -0.270263 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 93% (93/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 46 nu = 0.682693 obj = -12.995649, rho = -0.281057 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 94% (94/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 50 nu = 0.596734 obj = -16.198289, rho = -0.318933 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 36 nu = 0.522767 obj = -20.123503, rho = -0.172246 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 97% (97/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 46 nu = 0.451046 obj = -24.746530, rho = -0.107733 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 99.3% (993/1000) (classification) * optimization finished, #iter = 42 nu = 0.381102 obj = -30.507035, rho = -0.184534 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 87 nu = 0.328090 obj = -37.779379, rho = -0.182513 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 42 nu = 0.290474 obj = -46.505896, rho = -0.212622 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 88 nu = 0.249161 obj = -56.053909, rho = -0.217708 nSV = 30, nBSV = 21 Total nSV = 30 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.214531 obj = -66.520029, rho = -0.169875 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 162 nu = 0.175292 obj = -77.755858, rho = -0.068680 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 85 nu = 0.139313 obj = -92.241259, rho = -0.059051 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.966197, rho = -0.020834 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 95% (95/100) (classification) Accuracy = 89.1% (891/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.368507, rho = -0.029969 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 95% (95/100) (classification) Accuracy = 89.1% (891/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.924416, rho = -0.043109 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 95% (95/100) (classification) Accuracy = 89.1% (891/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.676901, rho = -0.062010 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 95% (95/100) (classification) Accuracy = 89.1% (891/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.661729, rho = -0.089199 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 95% (95/100) (classification) Accuracy = 89.1% (891/1000) (classification) * optimization finished, #iter = 50 nu = 0.996723 obj = -4.876542, rho = -0.126587 nSV = 100, nBSV = 98 Total nSV = 100 Accuracy = 95% (95/100) (classification) Accuracy = 89.2% (892/1000) (classification) * optimization finished, #iter = 50 nu = 0.950122 obj = -6.304943, rho = -0.152017 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 98% (98/100) (classification) Accuracy = 90.6% (906/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -7.977604, rho = -0.199985 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 100% (100/100) (classification) Accuracy = 93.7% (937/1000) (classification) * optimization finished, #iter = 44 nu = 0.774063 obj = -9.898147, rho = -0.147674 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 100% (100/100) (classification) Accuracy = 94.4% (944/1000) (classification) * optimization finished, #iter = 43 nu = 0.672942 obj = -12.101608, rho = -0.190164 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 38 nu = 0.573435 obj = -14.681401, rho = -0.144745 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 37 nu = 0.480000 obj = -17.739442, rho = -0.141319 nSV = 50, nBSV = 47 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 33 nu = 0.411007 obj = -21.267177, rho = -0.164198 nSV = 43, nBSV = 39 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 42 nu = 0.341696 obj = -25.157306, rho = -0.210075 nSV = 38, nBSV = 33 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 52 nu = 0.281876 obj = -29.677603, rho = -0.251327 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 27 nu = 0.229701 obj = -35.356740, rho = -0.262739 nSV = 25, nBSV = 22 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 56 nu = 0.189386 obj = -42.168297, rho = -0.263636 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 56 nu = 0.157034 obj = -50.168801, rho = -0.260258 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 68 nu = 0.130840 obj = -59.856636, rho = -0.177014 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 59 nu = 0.107467 obj = -71.312804, rho = -0.225828 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -0.821969, rho = -0.953085 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -1.170988, rho = -0.932515 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -1.660877, rho = -0.902927 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -2.340401, rho = -0.860365 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -3.265806, rho = -0.799142 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 58% (58/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -4.489248, rho = -0.711075 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 59% (59/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -6.026246, rho = -0.584396 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 75% (75/100) (classification) Accuracy = 70.6% (706/1000) (classification) * optimization finished, #iter = 42 nu = 0.827731 obj = -7.780748, rho = -0.426556 nSV = 84, nBSV = 82 Total nSV = 84 Accuracy = 94% (94/100) (classification) Accuracy = 90.5% (905/1000) (classification) * optimization finished, #iter = 50 nu = 0.757165 obj = -9.654544, rho = -0.344860 nSV = 78, nBSV = 74 Total nSV = 78 Accuracy = 99% (99/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 46 nu = 0.659695 obj = -11.771587, rho = -0.273104 nSV = 68, nBSV = 64 Total nSV = 68 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 40 nu = 0.561235 obj = -14.173807, rho = -0.198106 nSV = 58, nBSV = 54 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 40 nu = 0.483672 obj = -16.810373, rho = -0.237522 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 61 nu = 0.395726 obj = -19.604175, rho = -0.271078 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 70 nu = 0.321670 obj = -22.804654, rho = -0.348813 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 29 nu = 0.255749 obj = -26.531582, rho = -0.330760 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 79 nu = 0.209695 obj = -30.668992, rho = -0.348506 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 140 nu = 0.169658 obj = -35.244215, rho = -0.384689 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 88 nu = 0.134792 obj = -40.843320, rho = -0.447641 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*..* optimization finished, #iter = 340 nu = 0.111482 obj = -46.835750, rho = -0.454293 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.089831 obj = -53.064816, rho = -0.511850 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -0.913822, rho = -0.923540 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.297977, rho = -0.890016 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.832912, rho = -0.841794 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.565866, rho = -0.772428 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.544611, rho = -0.672650 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -4.796115, rho = -0.529123 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 76% (76/100) (classification) Accuracy = 70.4% (704/1000) (classification) * optimization finished, #iter = 53 nu = 0.936673 obj = -6.272930, rho = -0.327869 nSV = 95, nBSV = 92 Total nSV = 95 Accuracy = 94% (94/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 45 nu = 0.860589 obj = -7.915955, rho = -0.203031 nSV = 89, nBSV = 85 Total nSV = 89 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 57 nu = 0.766966 obj = -9.755839, rho = -0.118027 nSV = 79, nBSV = 75 Total nSV = 79 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 38 nu = 0.665228 obj = -11.919039, rho = -0.145962 nSV = 68, nBSV = 65 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 51 nu = 0.562547 obj = -14.356153, rho = -0.118757 nSV = 60, nBSV = 53 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 29 nu = 0.480000 obj = -17.206651, rho = -0.222033 nSV = 49, nBSV = 47 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 50 nu = 0.397495 obj = -20.284814, rho = -0.195425 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 73 nu = 0.321576 obj = -24.132416, rho = -0.220476 nSV = 38, nBSV = 29 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 33 nu = 0.272057 obj = -28.822658, rho = -0.285636 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.228649 obj = -34.038106, rho = -0.254117 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 85 nu = 0.185031 obj = -40.170666, rho = -0.302257 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.161179 obj = -45.712577, rho = -0.173188 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 174 nu = 0.123256 obj = -51.081238, rho = -0.187266 nSV = 18, nBSV = 7 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 66 nu = 0.096263 obj = -57.973090, rho = -0.247589 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.970870, rho = -0.033102 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 92% (92/100) (classification) Accuracy = 90.9% (909/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.378177, rho = -0.047616 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 92% (92/100) (classification) Accuracy = 90.9% (909/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.944425, rho = -0.068493 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 92% (92/100) (classification) Accuracy = 90.9% (909/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.718302, rho = -0.098523 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 92% (92/100) (classification) Accuracy = 90.9% (909/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.747393, rho = -0.141721 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 92% (92/100) (classification) Accuracy = 90.9% (909/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -5.053688, rho = -0.203858 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 92% (92/100) (classification) Accuracy = 90.9% (909/1000) (classification) * optimization finished, #iter = 48 nu = 0.955928 obj = -6.624819, rho = -0.274777 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 91% (91/100) (classification) Accuracy = 92.9% (929/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -8.596477, rho = -0.275883 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 96% (96/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 42 nu = 0.805058 obj = -11.010181, rho = -0.238731 nSV = 82, nBSV = 79 Total nSV = 82 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 47 nu = 0.739809 obj = -13.793721, rho = -0.310736 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 57 nu = 0.639076 obj = -17.000881, rho = -0.326853 nSV = 67, nBSV = 62 Total nSV = 67 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 45 nu = 0.558606 obj = -20.811427, rho = -0.214431 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.476377 obj = -25.270209, rho = -0.203901 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.399727 obj = -30.603976, rho = -0.286574 nSV = 44, nBSV = 35 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.339380 obj = -37.118151, rho = -0.326771 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 97 nu = 0.281180 obj = -44.955175, rho = -0.356486 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 71 nu = 0.238584 obj = -54.680657, rho = -0.356172 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 99 nu = 0.204198 obj = -66.602966, rho = -0.342398 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 126 nu = 0.168573 obj = -80.844628, rho = -0.385268 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 201 nu = 0.142626 obj = -98.393744, rho = -0.298640 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -0.914637, rho = 0.880436 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.299665, rho = 0.827485 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.836405, rho = 0.751846 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.573093, rho = 0.643043 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.559564, rho = 0.486535 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 54% (54/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -4.827055, rho = 0.261406 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 70% (70/100) (classification) Accuracy = 67.2% (672/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -6.349706, rho = 0.012373 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 91% (91/100) (classification) Accuracy = 88% (880/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -8.076448, rho = -0.186750 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 97% (97/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 49 nu = 0.780000 obj = -9.983396, rho = -0.246413 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 39 nu = 0.671312 obj = -12.233353, rho = -0.191138 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.578054 obj = -14.896014, rho = -0.151661 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 33 nu = 0.492237 obj = -17.980810, rho = -0.098430 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 27 nu = 0.412662 obj = -21.534765, rho = -0.121667 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 35 nu = 0.342586 obj = -25.796316, rho = -0.124378 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 37 nu = 0.285013 obj = -30.850171, rho = -0.180416 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.239390 obj = -37.024377, rho = -0.146783 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..*.* optimization finished, #iter = 332 nu = 0.195034 obj = -44.427280, rho = -0.136542 nSV = 25, nBSV = 15 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.158546 obj = -54.369696, rho = -0.090541 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 125 nu = 0.133844 obj = -68.057034, rho = -0.092640 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.116832 obj = -85.812735, rho = -0.102701 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -0.842365, rho = 0.916504 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -1.200578, rho = 0.879896 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -1.703959, rho = 0.827236 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -2.403443, rho = 0.751488 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -3.358707, rho = 0.642528 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -4.627468, rho = 0.485794 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 52.1% (521/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -6.234560, rho = 0.260340 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 78% (78/100) (classification) Accuracy = 76.7% (767/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -8.095296, rho = -0.063964 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 99% (99/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 44 nu = 0.794530 obj = -10.139625, rho = -0.058604 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 49 nu = 0.685820 obj = -12.415222, rho = -0.065604 nSV = 72, nBSV = 65 Total nSV = 72 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 37 nu = 0.591975 obj = -15.031299, rho = -0.056750 nSV = 61, nBSV = 58 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 34 nu = 0.500000 obj = -17.998055, rho = -0.006384 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 33 nu = 0.420902 obj = -21.286071, rho = -0.029384 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.344755 obj = -24.804229, rho = -0.108648 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 42 nu = 0.286832 obj = -28.815028, rho = -0.168626 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 88 nu = 0.232464 obj = -32.645476, rho = -0.205337 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.........* optimization finished, #iter = 933 nu = 0.180556 obj = -36.897366, rho = -0.223469 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.143057 obj = -42.161609, rho = -0.278848 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 140 nu = 0.109970 obj = -48.742899, rho = -0.276529 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.089085 obj = -57.211553, rho = -0.257002 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 47 nu = 0.860000 obj = -0.839378, rho = 0.910605 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 47 nu = 0.860000 obj = -1.194396, rho = 0.871410 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 48 nu = 0.860000 obj = -1.691168, rho = 0.814916 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 57% (57/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 48 nu = 0.860000 obj = -2.376977, rho = 0.733765 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 57% (57/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 49 nu = 0.860000 obj = -3.303949, rho = 0.617008 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 57% (57/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 49 nu = 0.860000 obj = -4.514166, rho = 0.449086 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 64% (64/100) (classification) Accuracy = 58% (580/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -6.000125, rho = 0.207622 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 88% (88/100) (classification) Accuracy = 83.1% (831/1000) (classification) * optimization finished, #iter = 49 nu = 0.828028 obj = -7.643307, rho = 0.068684 nSV = 85, nBSV = 81 Total nSV = 85 Accuracy = 96% (96/100) (classification) Accuracy = 94.3% (943/1000) (classification) * optimization finished, #iter = 44 nu = 0.733159 obj = -9.509854, rho = 0.062470 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 97% (97/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 46 nu = 0.630495 obj = -11.754673, rho = 0.134264 nSV = 64, nBSV = 61 Total nSV = 64 Accuracy = 97% (97/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 42 nu = 0.552912 obj = -14.520145, rho = 0.085328 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 58 nu = 0.464928 obj = -17.835233, rho = 0.064742 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 54 nu = 0.401255 obj = -21.922431, rho = 0.005791 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 54 nu = 0.341500 obj = -26.929220, rho = 0.043184 nSV = 39, nBSV = 30 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 41 nu = 0.283254 obj = -33.491746, rho = 0.049544 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 56 nu = 0.253947 obj = -41.417097, rho = -0.129928 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 48 nu = 0.216289 obj = -51.336005, rho = -0.116373 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 46 nu = 0.189905 obj = -63.345272, rho = -0.197203 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 91 nu = 0.158770 obj = -77.609917, rho = -0.188862 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 85 nu = 0.135105 obj = -95.964326, rho = -0.275845 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 40 nu = 0.800000 obj = -0.784427, rho = -0.963795 nSV = 80, nBSV = 80 Total nSV = 80 Accuracy = 60% (60/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 40 nu = 0.800000 obj = -1.118537, rho = -0.947922 nSV = 80, nBSV = 80 Total nSV = 80 Accuracy = 60% (60/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 40 nu = 0.800000 obj = -1.588638, rho = -0.925088 nSV = 80, nBSV = 80 Total nSV = 80 Accuracy = 60% (60/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 40 nu = 0.800000 obj = -2.243126, rho = -0.892242 nSV = 80, nBSV = 80 Total nSV = 80 Accuracy = 60% (60/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 40 nu = 0.800000 obj = -3.139618, rho = -0.844996 nSV = 80, nBSV = 80 Total nSV = 80 Accuracy = 60% (60/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 40 nu = 0.800000 obj = -4.336154, rho = -0.777035 nSV = 80, nBSV = 80 Total nSV = 80 Accuracy = 61% (61/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 40 nu = 0.800000 obj = -5.864835, rho = -0.679276 nSV = 80, nBSV = 80 Total nSV = 80 Accuracy = 78% (78/100) (classification) Accuracy = 68.7% (687/1000) (classification) * optimization finished, #iter = 40 nu = 0.786238 obj = -7.670953, rho = -0.554012 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 88% (88/100) (classification) Accuracy = 87.3% (873/1000) (classification) * optimization finished, #iter = 41 nu = 0.726102 obj = -9.793422, rho = -0.513673 nSV = 74, nBSV = 71 Total nSV = 74 Accuracy = 93% (93/100) (classification) Accuracy = 92.6% (926/1000) (classification) * optimization finished, #iter = 51 nu = 0.646901 obj = -12.306982, rho = -0.479657 nSV = 67, nBSV = 61 Total nSV = 67 Accuracy = 96% (96/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 43 nu = 0.566558 obj = -15.328280, rho = -0.429017 nSV = 58, nBSV = 54 Total nSV = 58 Accuracy = 96% (96/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 39 nu = 0.496721 obj = -18.815867, rho = -0.294092 nSV = 55, nBSV = 48 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 92 nu = 0.419729 obj = -23.155421, rho = -0.269423 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 38 nu = 0.359805 obj = -28.542434, rho = -0.253067 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 54 nu = 0.309139 obj = -35.166682, rho = -0.309626 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 33 nu = 0.260563 obj = -43.611404, rho = -0.343526 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 40 nu = 0.228800 obj = -53.850305, rho = -0.333309 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 34 nu = 0.197811 obj = -66.117856, rho = -0.343231 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 51 nu = 0.170262 obj = -80.571640, rho = -0.347791 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 193 nu = 0.140865 obj = -97.511492, rho = -0.384569 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -0.915424, rho = 0.875390 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -1.301291, rho = 0.820755 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -1.839771, rho = 0.742165 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.580058, rho = 0.629117 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.573975, rho = 0.466504 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -4.856874, rho = 0.232592 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 79% (79/100) (classification) Accuracy = 77.5% (775/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.398506, rho = -0.103878 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -8.133144, rho = -0.110429 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 58 nu = 0.803742 obj = -10.083827, rho = -0.056047 nSV = 83, nBSV = 77 Total nSV = 83 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 50 nu = 0.695183 obj = -12.220747, rho = -0.032385 nSV = 73, nBSV = 67 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 50 nu = 0.586132 obj = -14.606508, rho = -0.040404 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 48 nu = 0.485718 obj = -17.429989, rho = -0.022480 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.402095 obj = -20.763314, rho = 0.069123 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 27 nu = 0.336870 obj = -24.618617, rho = 0.046047 nSV = 36, nBSV = 32 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 36 nu = 0.284644 obj = -28.796579, rho = 0.098085 nSV = 30, nBSV = 26 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 54 nu = 0.227122 obj = -33.249412, rho = 0.132599 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 51 nu = 0.183516 obj = -38.564827, rho = 0.145903 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 98 nu = 0.147643 obj = -44.359472, rho = 0.073808 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 201 nu = 0.119987 obj = -50.837785, rho = 0.051101 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*...* optimization finished, #iter = 466 nu = 0.097412 obj = -57.745171, rho = -0.020885 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -0.895865, rho = -0.931405 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.273435, rho = -0.901329 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.800277, rho = -0.858067 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -2.524439, rho = -0.795837 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -3.496435, rho = -0.706321 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -4.750435, rho = -0.577558 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 68% (68/100) (classification) Accuracy = 58.9% (589/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -6.255951, rho = -0.392338 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 92% (92/100) (classification) Accuracy = 91.3% (913/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -7.908621, rho = -0.294055 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 43 nu = 0.773558 obj = -9.722594, rho = -0.262057 nSV = 79, nBSV = 75 Total nSV = 79 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 43 nu = 0.662797 obj = -11.731167, rho = -0.249618 nSV = 69, nBSV = 63 Total nSV = 69 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 33 nu = 0.554624 obj = -14.194304, rho = -0.229456 nSV = 57, nBSV = 54 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 38 nu = 0.470541 obj = -17.003631, rho = -0.238406 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 34 nu = 0.394276 obj = -20.307338, rho = -0.130824 nSV = 41, nBSV = 37 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 31 nu = 0.332378 obj = -23.891802, rho = -0.056207 nSV = 35, nBSV = 31 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 56 nu = 0.268591 obj = -27.894868, rho = -0.095367 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 72 nu = 0.221838 obj = -32.380321, rho = -0.140354 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 58 nu = 0.177437 obj = -37.668954, rho = -0.154973 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 93 nu = 0.143152 obj = -43.882491, rho = -0.153330 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 59 nu = 0.118647 obj = -50.901568, rho = -0.128486 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 52 nu = 0.096777 obj = -57.839931, rho = -0.091799 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -0.804645, rho = 0.923130 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -1.147756, rho = 0.889426 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -1.630952, rho = 0.840944 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -2.304580, rho = 0.771207 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -3.229234, rho = 0.670930 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 59% (59/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -4.467579, rho = 0.526650 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 59% (59/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -6.059093, rho = 0.318123 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 72% (72/100) (classification) Accuracy = 67.2% (672/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -7.955713, rho = 0.018744 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 90% (90/100) (classification) Accuracy = 91% (910/1000) (classification) * optimization finished, #iter = 48 nu = 0.760000 obj = -10.081839, rho = -0.084546 nSV = 77, nBSV = 74 Total nSV = 77 Accuracy = 97% (97/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 42 nu = 0.681341 obj = -12.510182, rho = -0.142851 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 35 nu = 0.582163 obj = -15.332966, rho = -0.197110 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 37 nu = 0.499989 obj = -18.753946, rho = -0.215023 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 41 nu = 0.422832 obj = -22.857082, rho = -0.169228 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.352257 obj = -27.932659, rho = -0.171498 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 34 nu = 0.308356 obj = -34.292802, rho = -0.210543 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 53 nu = 0.260591 obj = -41.638284, rho = -0.252435 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 41 nu = 0.224483 obj = -50.087212, rho = -0.294745 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 87 nu = 0.192167 obj = -59.577742, rho = -0.395222 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.161438 obj = -69.420358, rho = -0.302544 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.137254 obj = -79.353383, rho = -0.554932 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -0.892060, rho = -0.938784 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.265561, rho = -0.911944 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.783985, rho = -0.873336 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -2.490728, rho = -0.817800 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -3.426682, rho = -0.737915 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 55% (55/100) (classification) Accuracy = 53% (530/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -4.606108, rho = -0.623003 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 82% (82/100) (classification) Accuracy = 74.3% (743/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -5.984583, rho = -0.495330 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 96% (96/100) (classification) Accuracy = 88.7% (887/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -7.542984, rho = -0.442500 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 98% (98/100) (classification) Accuracy = 93.7% (937/1000) (classification) * optimization finished, #iter = 46 nu = 0.725184 obj = -9.379362, rho = -0.400847 nSV = 74, nBSV = 71 Total nSV = 74 Accuracy = 99% (99/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 37 nu = 0.621319 obj = -11.583405, rho = -0.381528 nSV = 64, nBSV = 61 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 43 nu = 0.557383 obj = -14.208687, rho = -0.269693 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 31 nu = 0.468940 obj = -17.098253, rho = -0.282903 nSV = 48, nBSV = 45 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.396057 obj = -20.388209, rho = -0.317171 nSV = 44, nBSV = 36 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 51 nu = 0.328753 obj = -24.234128, rho = -0.357270 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 60 nu = 0.272064 obj = -28.598966, rho = -0.327582 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 90 nu = 0.228527 obj = -33.184747, rho = -0.354585 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 55 nu = 0.187212 obj = -37.970496, rho = -0.359788 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 96 nu = 0.152665 obj = -42.453945, rho = -0.375390 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 273 nu = 0.117676 obj = -46.437812, rho = -0.404385 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 162 nu = 0.088443 obj = -50.412172, rho = -0.418841 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -0.950969, rho = 0.846553 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.349611, rho = 0.779274 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.903462, rho = 0.682496 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.659644, rho = 0.543287 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.663565, rho = 0.343041 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 55% (55/100) (classification) Accuracy = 53.5% (535/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.934240, rho = 0.054998 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 83% (83/100) (classification) Accuracy = 83.9% (839/1000) (classification) * optimization finished, #iter = 51 nu = 0.954630 obj = -6.428811, rho = -0.231626 nSV = 97, nBSV = 94 Total nSV = 97 Accuracy = 95% (95/100) (classification) Accuracy = 95.1% (951/1000) (classification) * optimization finished, #iter = 46 nu = 0.871738 obj = -8.202295, rho = -0.363107 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 96% (96/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 48 nu = 0.789118 obj = -10.258188, rho = -0.320949 nSV = 82, nBSV = 78 Total nSV = 82 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 48 nu = 0.698122 obj = -12.605182, rho = -0.221051 nSV = 72, nBSV = 68 Total nSV = 72 Accuracy = 96% (96/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 43 nu = 0.588589 obj = -15.255586, rho = -0.190955 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 51 nu = 0.504368 obj = -18.379332, rho = -0.164896 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 96% (96/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 40 nu = 0.425070 obj = -22.058229, rho = -0.137707 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 54 nu = 0.352059 obj = -26.355144, rho = -0.136624 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 46 nu = 0.291315 obj = -31.707872, rho = -0.113625 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 51 nu = 0.243245 obj = -38.073760, rho = -0.221147 nSV = 27, nBSV = 22 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 138 nu = 0.206398 obj = -45.568757, rho = -0.273404 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.167372 obj = -54.485776, rho = -0.286256 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.142389 obj = -65.436902, rho = -0.306179 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.122097 obj = -78.227418, rho = -0.407574 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -0.910866, rho = 0.857002 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -1.291860, rho = 0.794305 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -1.820257, rho = 0.704118 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -2.539680, rho = 0.574388 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -3.490429, rho = 0.387778 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 57% (57/100) (classification) Accuracy = 53.2% (532/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.684004, rho = 0.119350 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 86% (86/100) (classification) Accuracy = 77.5% (775/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -6.049414, rho = -0.128200 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 96% (96/100) (classification) Accuracy = 93.3% (933/1000) (classification) * optimization finished, #iter = 45 nu = 0.835355 obj = -7.577141, rho = -0.124915 nSV = 84, nBSV = 82 Total nSV = 84 Accuracy = 97% (97/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 43 nu = 0.736715 obj = -9.313903, rho = -0.099944 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 43 nu = 0.639495 obj = -11.293796, rho = -0.089735 nSV = 66, nBSV = 61 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.536031 obj = -13.556401, rho = -0.084715 nSV = 57, nBSV = 51 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 40 nu = 0.454657 obj = -16.186904, rho = -0.051822 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 61 nu = 0.372685 obj = -19.222278, rho = -0.061044 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 55 nu = 0.307926 obj = -22.809091, rho = -0.034507 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 67 nu = 0.253235 obj = -27.266640, rho = -0.025523 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 49 nu = 0.209790 obj = -32.611729, rho = -0.015035 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.173296 obj = -39.258163, rho = 0.039362 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 82 nu = 0.148907 obj = -47.014331, rho = 0.236544 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 88 nu = 0.120280 obj = -56.291903, rho = 0.265906 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 35 nu = 0.101393 obj = -68.171364, rho = 0.343203 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -0.877292, rho = 0.881214 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -1.247620, rho = 0.829132 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -1.765005, rho = 0.754215 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -2.477557, rho = 0.646450 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -3.436972, rho = 0.491436 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 56% (56/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -4.681403, rho = 0.268456 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 74% (74/100) (classification) Accuracy = 65.3% (653/1000) (classification) * optimization finished, #iter = 45 nu = 0.888720 obj = -6.192664, rho = -0.000495 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 91% (91/100) (classification) Accuracy = 86.4% (864/1000) (classification) * optimization finished, #iter = 46 nu = 0.840000 obj = -7.932984, rho = -0.145935 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 93% (93/100) (classification) Accuracy = 94.1% (941/1000) (classification) * optimization finished, #iter = 40 nu = 0.748602 obj = -9.995424, rho = -0.176942 nSV = 77, nBSV = 74 Total nSV = 77 Accuracy = 94% (94/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 50 nu = 0.662578 obj = -12.438575, rho = -0.158291 nSV = 70, nBSV = 64 Total nSV = 70 Accuracy = 96% (96/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 53 nu = 0.570381 obj = -15.416059, rho = -0.135845 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 96% (96/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 46 nu = 0.494524 obj = -19.087095, rho = -0.132205 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 96% (96/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 35 nu = 0.432973 obj = -23.535103, rho = -0.137943 nSV = 45, nBSV = 41 Total nSV = 45 Accuracy = 96% (96/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 48 nu = 0.380261 obj = -28.444781, rho = -0.127939 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 74 nu = 0.311829 obj = -34.089800, rho = -0.152794 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 62 nu = 0.258604 obj = -41.374364, rho = -0.170868 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 212 nu = 0.216186 obj = -50.273153, rho = -0.160519 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 163 nu = 0.185190 obj = -61.724082, rho = -0.127030 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 58 nu = 0.163191 obj = -74.431238, rho = -0.190588 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 129 nu = 0.139563 obj = -86.928028, rho = -0.344534 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -0.898427, rho = 0.884083 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.278736, rho = 0.833259 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.811245, rho = 0.760151 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -2.547134, rho = 0.654989 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -3.543393, rho = 0.503719 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -4.847599, rho = 0.286124 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 71% (71/100) (classification) Accuracy = 62.6% (626/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -6.456997, rho = -0.026874 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 94% (94/100) (classification) Accuracy = 90% (900/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -8.336697, rho = -0.094628 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 100% (100/100) (classification) Accuracy = 93.9% (939/1000) (classification) * optimization finished, #iter = 45 nu = 0.788123 obj = -10.558074, rho = -0.103804 nSV = 81, nBSV = 77 Total nSV = 81 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 42 nu = 0.696471 obj = -13.218493, rho = -0.004970 nSV = 71, nBSV = 66 Total nSV = 71 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 33 nu = 0.624486 obj = -16.298709, rho = 0.030633 nSV = 64, nBSV = 62 Total nSV = 64 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.525365 obj = -19.806608, rho = 0.038556 nSV = 57, nBSV = 51 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 45 nu = 0.460483 obj = -23.798749, rho = 0.065916 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.388654 obj = -28.240531, rho = 0.115024 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.317365 obj = -33.052238, rho = 0.048929 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 60 nu = 0.261056 obj = -38.466187, rho = -0.017691 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 60 nu = 0.215685 obj = -44.500562, rho = -0.043205 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 125 nu = 0.174681 obj = -50.597821, rho = -0.038217 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 199 nu = 0.137850 obj = -56.386291, rho = -0.002139 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *...* optimization finished, #iter = 392 nu = 0.107723 obj = -62.143180, rho = 0.036243 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -0.951354, rho = 0.839353 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.350409, rho = 0.768917 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.905114, rho = 0.667599 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.663062, rho = 0.521858 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.670636, rho = 0.312216 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 56% (56/100) (classification) Accuracy = 56.7% (567/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.948871, rho = 0.010658 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 89% (89/100) (classification) Accuracy = 84.8% (848/1000) (classification) * optimization finished, #iter = 50 nu = 0.962023 obj = -6.436991, rho = -0.285510 nSV = 98, nBSV = 95 Total nSV = 98 Accuracy = 93% (93/100) (classification) Accuracy = 94.3% (943/1000) (classification) * optimization finished, #iter = 48 nu = 0.871664 obj = -8.195625, rho = -0.301849 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 96% (96/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 48 nu = 0.789877 obj = -10.256841, rho = -0.242308 nSV = 81, nBSV = 76 Total nSV = 81 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 47 nu = 0.703488 obj = -12.640796, rho = -0.203333 nSV = 72, nBSV = 68 Total nSV = 72 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 43 nu = 0.592113 obj = -15.278023, rho = -0.164078 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 55 nu = 0.502690 obj = -18.509084, rho = -0.144736 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 33 nu = 0.420000 obj = -22.479508, rho = -0.137345 nSV = 44, nBSV = 41 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 36 nu = 0.356621 obj = -27.146229, rho = -0.107252 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 67 nu = 0.298615 obj = -32.708720, rho = -0.061309 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 90 nu = 0.246353 obj = -39.731523, rho = -0.063159 nSV = 30, nBSV = 20 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 55 nu = 0.206313 obj = -48.948337, rho = -0.046933 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 34 nu = 0.184466 obj = -60.132034, rho = -0.063756 nSV = 22, nBSV = 18 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 83 nu = 0.152905 obj = -72.315271, rho = -0.093216 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 79 nu = 0.124699 obj = -88.960142, rho = -0.124562 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.945329, rho = 0.833660 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 48.1% (481/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.337942, rho = 0.760728 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 48.1% (481/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.879317, rho = 0.655820 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 48.1% (481/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.609684, rho = 0.504914 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 48.1% (481/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.560191, rho = 0.287843 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 67% (67/100) (classification) Accuracy = 59.7% (597/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.720344, rho = -0.024402 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 93% (93/100) (classification) Accuracy = 90.1% (901/1000) (classification) * optimization finished, #iter = 52 nu = 0.927100 obj = -6.015391, rho = -0.209242 nSV = 94, nBSV = 91 Total nSV = 94 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 48 nu = 0.819818 obj = -7.551033, rho = -0.177153 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 41 nu = 0.736248 obj = -9.374116, rho = -0.167804 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 57 nu = 0.635271 obj = -11.424415, rho = -0.105911 nSV = 68, nBSV = 62 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 51 nu = 0.540780 obj = -13.807703, rho = -0.149919 nSV = 57, nBSV = 51 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 31 nu = 0.457247 obj = -16.589941, rho = -0.174757 nSV = 47, nBSV = 44 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 33 nu = 0.381785 obj = -19.874227, rho = -0.103679 nSV = 40, nBSV = 37 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 34 nu = 0.318253 obj = -23.653151, rho = -0.217580 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 31 nu = 0.264689 obj = -28.171835, rho = -0.202180 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 43 nu = 0.222472 obj = -32.829392, rho = -0.170340 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 41 nu = 0.179831 obj = -38.495891, rho = -0.231003 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 61 nu = 0.145031 obj = -44.867024, rho = -0.312845 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 87 nu = 0.117833 obj = -52.829119, rho = -0.359539 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 82 nu = 0.097367 obj = -61.865981, rho = -0.468241 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -0.932180, rho = 0.885570 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 47.9% (479/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.323349, rho = 0.835399 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 47.9% (479/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.867267, rho = 0.763229 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 47.9% (479/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -2.610851, rho = 0.659417 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 47.9% (479/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -3.600148, rho = 0.510088 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 48.3% (483/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -4.857025, rho = 0.295286 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 77% (77/100) (classification) Accuracy = 75% (750/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -6.321137, rho = -0.013695 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 98% (98/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 48 nu = 0.863424 obj = -7.929710, rho = -0.083351 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 48 nu = 0.762553 obj = -9.831652, rho = -0.050564 nSV = 79, nBSV = 74 Total nSV = 79 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 37 nu = 0.672765 obj = -12.062436, rho = 0.022260 nSV = 69, nBSV = 66 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 39 nu = 0.576401 obj = -14.483849, rho = -0.065538 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 43 nu = 0.487361 obj = -17.253044, rho = -0.013784 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 51 nu = 0.406828 obj = -20.276930, rho = 0.025764 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 31 nu = 0.328117 obj = -23.758567, rho = 0.012402 nSV = 36, nBSV = 32 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 42 nu = 0.269954 obj = -27.828187, rho = -0.032302 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 75 nu = 0.222801 obj = -32.245013, rho = -0.039911 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 130 nu = 0.176965 obj = -37.009038, rho = -0.018581 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 173 nu = 0.140006 obj = -42.872437, rho = 0.003226 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 95 nu = 0.110798 obj = -50.538283, rho = -0.056343 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 78 nu = 0.090847 obj = -60.465359, rho = -0.032729 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.964260, rho = -0.042750 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 95% (95/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.364498, rho = -0.061494 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 95% (95/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.916121, rho = -0.088456 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 95% (95/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.659739, rho = -0.127240 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 95% (95/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.626217, rho = -0.183028 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 95% (95/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.811379, rho = -0.231959 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 96% (96/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 50 nu = 0.934423 obj = -6.183604, rho = -0.137057 nSV = 95, nBSV = 92 Total nSV = 95 Accuracy = 96% (96/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 49 nu = 0.847249 obj = -7.795323, rho = -0.109749 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 96% (96/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 43 nu = 0.757125 obj = -9.629158, rho = -0.112421 nSV = 76, nBSV = 74 Total nSV = 76 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 38 nu = 0.658377 obj = -11.720623, rho = -0.097871 nSV = 67, nBSV = 64 Total nSV = 67 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 39 nu = 0.555890 obj = -14.121655, rho = -0.107360 nSV = 58, nBSV = 54 Total nSV = 58 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 36 nu = 0.470985 obj = -16.886577, rho = -0.091489 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 60 nu = 0.389196 obj = -19.925324, rho = -0.023365 nSV = 44, nBSV = 36 Total nSV = 44 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 190 nu = 0.321538 obj = -23.620179, rho = -0.055819 nSV = 37, nBSV = 28 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 44 nu = 0.263123 obj = -27.943349, rho = -0.103806 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 87 nu = 0.209871 obj = -33.389642, rho = -0.078642 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 66 nu = 0.175109 obj = -40.766548, rho = -0.135851 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*..* optimization finished, #iter = 318 nu = 0.147453 obj = -49.892215, rho = -0.132105 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 150 nu = 0.124448 obj = -61.934103, rho = -0.114861 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.107739 obj = -77.063262, rho = -0.109630 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.949295, rho = 0.847058 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.346148, rho = 0.780001 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.896296, rho = 0.683543 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.644817, rho = 0.544792 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.632885, rho = 0.345206 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 56% (56/100) (classification) Accuracy = 57.2% (572/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.870759, rho = 0.058112 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 94% (94/100) (classification) Accuracy = 88.1% (881/1000) (classification) * optimization finished, #iter = 50 nu = 0.955920 obj = -6.297713, rho = -0.185923 nSV = 96, nBSV = 93 Total nSV = 96 Accuracy = 97% (97/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 56 nu = 0.876616 obj = -7.909084, rho = -0.165371 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 51 nu = 0.768153 obj = -9.766792, rho = -0.170916 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 48 nu = 0.672738 obj = -11.776182, rho = -0.075535 nSV = 70, nBSV = 62 Total nSV = 70 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 53 nu = 0.557227 obj = -14.048909, rho = -0.069070 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 38 nu = 0.464839 obj = -16.784361, rho = -0.026388 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 58 nu = 0.383820 obj = -19.981623, rho = -0.001299 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 37 nu = 0.316583 obj = -24.007562, rho = 0.030204 nSV = 34, nBSV = 30 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 27 nu = 0.270302 obj = -28.545469, rho = 0.113485 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.221443 obj = -33.939076, rho = 0.040109 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 99 nu = 0.181437 obj = -40.361946, rho = -0.011175 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.147425 obj = -48.770994, rho = -0.009946 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 95 nu = 0.127543 obj = -58.217971, rho = -0.152789 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.104798 obj = -69.307163, rho = -0.312142 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -0.914119, rho = 0.876058 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.298591, rho = 0.821716 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.834183, rho = 0.743548 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.568496, rho = 0.631106 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.550051, rho = 0.469365 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.807372, rho = 0.236708 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 78% (78/100) (classification) Accuracy = 73.8% (738/1000) (classification) * optimization finished, #iter = 47 nu = 0.928966 obj = -6.297726, rho = -0.065124 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 94% (94/100) (classification) Accuracy = 93.9% (939/1000) (classification) * optimization finished, #iter = 45 nu = 0.857946 obj = -8.012788, rho = -0.064989 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 94% (94/100) (classification) Accuracy = 94.4% (944/1000) (classification) * optimization finished, #iter = 43 nu = 0.755410 obj = -10.092947, rho = -0.068035 nSV = 77, nBSV = 74 Total nSV = 77 Accuracy = 97% (97/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 41 nu = 0.677086 obj = -12.559595, rho = -0.083062 nSV = 68, nBSV = 65 Total nSV = 68 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 39 nu = 0.585522 obj = -15.398468, rho = -0.040812 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 32 nu = 0.501522 obj = -18.878321, rho = 0.010452 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 48 nu = 0.419954 obj = -23.223813, rho = 0.041981 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 42 nu = 0.357012 obj = -28.847404, rho = 0.096518 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 96% (96/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 48 nu = 0.306584 obj = -35.944959, rho = 0.004222 nSV = 33, nBSV = 29 Total nSV = 33 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 71 nu = 0.266770 obj = -44.727611, rho = -0.053598 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 59 nu = 0.236000 obj = -55.724775, rho = -0.048406 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) .*.* optimization finished, #iter = 299 nu = 0.206049 obj = -68.349268, rho = -0.216014 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 61 nu = 0.172614 obj = -83.756619, rho = -0.309435 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 92 nu = 0.146926 obj = -102.428509, rho = -0.478439 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.965378, rho = -0.004870 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 93.4% (934/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.366813, rho = -0.007005 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 93.4% (934/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.920911, rho = -0.010076 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 93.4% (934/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.669649, rho = -0.014494 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 93.4% (934/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.646723, rho = -0.020849 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 93.4% (934/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -4.845387, rho = -0.029990 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 93.4% (934/1000) (classification) * optimization finished, #iter = 51 nu = 0.953749 obj = -6.194977, rho = -0.033066 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 98% (98/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 51 nu = 0.856950 obj = -7.733673, rho = 0.008979 nSV = 88, nBSV = 84 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 42 nu = 0.757625 obj = -9.534778, rho = 0.051675 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 39 nu = 0.650682 obj = -11.563455, rho = -0.030364 nSV = 66, nBSV = 63 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.548907 obj = -13.887795, rho = 0.050508 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 37 nu = 0.460000 obj = -16.700232, rho = -0.017160 nSV = 48, nBSV = 45 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 60 nu = 0.388694 obj = -19.883955, rho = 0.028960 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 58 nu = 0.324911 obj = -23.384207, rho = 0.081064 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 41 nu = 0.262231 obj = -27.541249, rho = 0.063564 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 69 nu = 0.215596 obj = -32.256964, rho = -0.000871 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 46 nu = 0.181383 obj = -37.176843, rho = -0.105719 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 80 nu = 0.141697 obj = -42.499204, rho = -0.084965 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 162 nu = 0.112521 obj = -49.097406, rho = -0.088967 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.091455 obj = -56.781522, rho = -0.123436 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -0.915764, rho = 0.887893 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 53.6% (536/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.301995, rho = 0.838740 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 53.6% (536/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.841226, rho = 0.768035 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 53.6% (536/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.583069, rho = 0.666330 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 53.6% (536/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -3.580206, rho = 0.520033 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 53.6% (536/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.869765, rho = 0.309591 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 66% (66/100) (classification) Accuracy = 67.2% (672/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.425179, rho = 0.006881 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 92% (92/100) (classification) Accuracy = 90.6% (906/1000) (classification) * optimization finished, #iter = 48 nu = 0.877652 obj = -8.177909, rho = -0.129030 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 47 nu = 0.787123 obj = -10.229979, rho = -0.060329 nSV = 81, nBSV = 77 Total nSV = 81 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 62 nu = 0.690836 obj = -12.548828, rho = -0.134423 nSV = 72, nBSV = 67 Total nSV = 72 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.580000 obj = -15.431899, rho = -0.139511 nSV = 60, nBSV = 57 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 53 nu = 0.496488 obj = -18.982636, rho = -0.168849 nSV = 53, nBSV = 45 Total nSV = 53 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 87 nu = 0.420387 obj = -23.438144, rho = -0.120212 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 35 nu = 0.374417 obj = -28.732815, rho = -0.056149 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 45 nu = 0.324190 obj = -34.488560, rho = -0.010873 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 73 nu = 0.264133 obj = -41.045091, rho = -0.022260 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.222728 obj = -48.996042, rho = 0.017845 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 114 nu = 0.184662 obj = -58.218406, rho = 0.045181 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 151 nu = 0.149391 obj = -69.382044, rho = 0.013236 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 152 nu = 0.125655 obj = -82.843030, rho = -0.004014 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.967320, rho = -0.035431 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.370831, rho = -0.050965 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.929226, rho = -0.073311 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.686854, rho = -0.105454 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.682322, rho = -0.151691 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -4.919048, rho = -0.218199 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 48 nu = 0.940162 obj = -6.371843, rho = -0.193149 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 96% (96/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 45 nu = 0.857707 obj = -8.141011, rho = -0.220363 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 43 nu = 0.766813 obj = -10.281185, rho = -0.111518 nSV = 79, nBSV = 75 Total nSV = 79 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 49 nu = 0.685081 obj = -12.837785, rho = -0.057364 nSV = 71, nBSV = 66 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 34 nu = 0.597568 obj = -15.903267, rho = 0.015221 nSV = 61, nBSV = 58 Total nSV = 61 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 33 nu = 0.521083 obj = -19.506995, rho = 0.059585 nSV = 54, nBSV = 51 Total nSV = 54 Accuracy = 97% (97/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 39 nu = 0.443705 obj = -23.611101, rho = 0.006828 nSV = 49, nBSV = 42 Total nSV = 49 Accuracy = 97% (97/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 44 nu = 0.370451 obj = -28.555301, rho = 0.101786 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 97% (97/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 45 nu = 0.314060 obj = -34.659584, rho = 0.116578 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 97% (97/100) (classification) Accuracy = 99.4% (994/1000) (classification) * optimization finished, #iter = 77 nu = 0.271584 obj = -41.701655, rho = -0.002044 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 97% (97/100) (classification) Accuracy = 99.2% (992/1000) (classification) .*.* optimization finished, #iter = 215 nu = 0.217612 obj = -50.200436, rho = -0.018832 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 99.1% (991/1000) (classification) *.* optimization finished, #iter = 191 nu = 0.183118 obj = -61.376611, rho = -0.145071 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 86 nu = 0.153226 obj = -75.573303, rho = -0.202212 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 91 nu = 0.128458 obj = -94.552903, rho = -0.165059 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 97% (97/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 44 nu = 0.800000 obj = -0.787584, rho = 0.929591 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 44 nu = 0.800000 obj = -1.125070, rho = 0.898720 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 44 nu = 0.800000 obj = -1.602155, rho = 0.854314 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 44 nu = 0.800000 obj = -2.271094, rho = 0.790438 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 44 nu = 0.800000 obj = -3.197488, rho = 0.698555 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 44 nu = 0.800000 obj = -4.455896, rho = 0.566387 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -6.112598, rho = 0.376269 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 65% (65/100) (classification) Accuracy = 56.2% (562/1000) (classification) * optimization finished, #iter = 42 nu = 0.800000 obj = -8.178164, rho = 0.102794 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 83% (83/100) (classification) Accuracy = 82.8% (828/1000) (classification) * optimization finished, #iter = 39 nu = 0.780000 obj = -10.543381, rho = -0.154554 nSV = 78, nBSV = 78 Total nSV = 78 Accuracy = 94% (94/100) (classification) Accuracy = 94.7% (947/1000) (classification) * optimization finished, #iter = 44 nu = 0.692503 obj = -13.231162, rho = -0.135540 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 97% (97/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 41 nu = 0.606789 obj = -16.521018, rho = -0.071070 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 56 nu = 0.524907 obj = -20.624915, rho = -0.022241 nSV = 57, nBSV = 50 Total nSV = 57 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 52 nu = 0.451502 obj = -25.856054, rho = -0.020449 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 48 nu = 0.400000 obj = -32.295374, rho = -0.138410 nSV = 41, nBSV = 38 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 30 nu = 0.344384 obj = -40.178426, rho = -0.210516 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 39 nu = 0.300043 obj = -50.151724, rho = -0.255595 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 94 nu = 0.258237 obj = -62.272887, rho = -0.328909 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 84 nu = 0.230415 obj = -77.289355, rho = -0.522346 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 67 nu = 0.190005 obj = -95.552210, rho = -0.524361 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 180 nu = 0.162928 obj = -120.337866, rho = -0.526961 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -0.806551, rho = 0.935714 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -1.151700, rho = 0.907528 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 46 nu = 0.820000 obj = -1.639116, rho = 0.866687 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 46 nu = 0.820000 obj = -2.321473, rho = 0.808236 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 46 nu = 0.820000 obj = -3.264186, rho = 0.724158 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -4.539898, rho = 0.603215 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -6.208729, rho = 0.429244 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 66% (66/100) (classification) Accuracy = 60% (600/1000) (classification) * optimization finished, #iter = 46 nu = 0.820000 obj = -8.265331, rho = 0.178996 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 83% (83/100) (classification) Accuracy = 88.4% (884/1000) (classification) * optimization finished, #iter = 43 nu = 0.780000 obj = -10.624831, rho = -0.005253 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 95% (95/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 42 nu = 0.690841 obj = -13.410165, rho = -0.032670 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 96% (96/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 44 nu = 0.620000 obj = -16.745383, rho = -0.003643 nSV = 63, nBSV = 60 Total nSV = 63 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 34 nu = 0.544862 obj = -20.710847, rho = 0.006254 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 48 nu = 0.481113 obj = -25.200035, rho = 0.101633 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 53 nu = 0.399101 obj = -30.457224, rho = 0.092180 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 60 nu = 0.331027 obj = -36.956550, rho = 0.015183 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.280949 obj = -44.628818, rho = 0.115485 nSV = 32, nBSV = 23 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 69 nu = 0.237705 obj = -54.315547, rho = 0.093235 nSV = 27, nBSV = 22 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 72 nu = 0.202031 obj = -65.996802, rho = 0.174557 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 69 nu = 0.174617 obj = -78.905719, rho = 0.204131 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 149 nu = 0.147269 obj = -92.472058, rho = 0.184201 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -0.877985, rho = 0.908974 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -1.249052, rho = 0.869064 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -1.767969, rho = 0.811655 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -2.483688, rho = 0.729076 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 49 nu = 0.900000 obj = -3.449663, rho = 0.611253 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -4.707662, rho = 0.440807 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 59% (59/100) (classification) Accuracy = 55.5% (555/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -6.245128, rho = 0.195628 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 88% (88/100) (classification) Accuracy = 82.9% (829/1000) (classification) * optimization finished, #iter = 49 nu = 0.874889 obj = -7.911944, rho = -0.056328 nSV = 89, nBSV = 84 Total nSV = 89 Accuracy = 98% (98/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 46 nu = 0.761165 obj = -9.788099, rho = -0.072225 nSV = 78, nBSV = 74 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 40 nu = 0.669213 obj = -11.917471, rho = -0.168460 nSV = 69, nBSV = 66 Total nSV = 69 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 37 nu = 0.577148 obj = -14.243623, rho = -0.187138 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.482903 obj = -16.831275, rho = -0.106058 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 63 nu = 0.393759 obj = -19.630976, rho = -0.138219 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 82 nu = 0.321589 obj = -22.990214, rho = -0.119239 nSV = 36, nBSV = 27 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.255358 obj = -26.941227, rho = -0.101372 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 84 nu = 0.214848 obj = -31.992569, rho = -0.076730 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 59 nu = 0.174931 obj = -37.640075, rho = -0.078911 nSV = 20, nBSV = 15 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 56 nu = 0.142300 obj = -44.120100, rho = -0.134798 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 73 nu = 0.115086 obj = -51.559226, rho = 0.037697 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 61 nu = 0.091898 obj = -61.440002, rho = 0.041592 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.968195, rho = -0.042231 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 94.5% (945/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.372642, rho = -0.060746 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 94.5% (945/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.932972, rho = -0.087381 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 94.5% (945/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.694606, rho = -0.125693 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 94.5% (945/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.698362, rho = -0.180803 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 94.5% (945/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -4.952235, rho = -0.260076 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 94.5% (945/1000) (classification) * optimization finished, #iter = 50 nu = 0.964618 obj = -6.415962, rho = -0.231597 nSV = 98, nBSV = 96 Total nSV = 98 Accuracy = 99% (99/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 46 nu = 0.892385 obj = -8.071861, rho = -0.163934 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 48 nu = 0.783156 obj = -9.953532, rho = -0.178753 nSV = 81, nBSV = 77 Total nSV = 81 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 46 nu = 0.678404 obj = -12.078259, rho = -0.099828 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.569448 obj = -14.577558, rho = -0.111785 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 33 nu = 0.479659 obj = -17.642056, rho = -0.102322 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 69 nu = 0.412130 obj = -21.106560, rho = -0.030629 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 69 nu = 0.343715 obj = -25.031728, rho = -0.072950 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 38 nu = 0.279173 obj = -29.732324, rho = -0.091058 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 77 nu = 0.241363 obj = -34.805080, rho = -0.222709 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 96 nu = 0.193889 obj = -39.789381, rho = -0.273276 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 167 nu = 0.149558 obj = -45.929944, rho = -0.254782 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 45 nu = 0.119800 obj = -54.250561, rho = -0.234921 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 96 nu = 0.097994 obj = -64.256354, rho = -0.314386 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -0.915785, rho = -0.925122 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.302038, rho = -0.892292 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.841317, rho = -0.845067 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.583256, rho = -0.777136 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.580593, rho = -0.679422 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -4.870567, rho = -0.538864 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 66% (66/100) (classification) Accuracy = 71.2% (712/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -6.426839, rho = -0.336679 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 96% (96/100) (classification) Accuracy = 94.3% (943/1000) (classification) * optimization finished, #iter = 45 nu = 0.884166 obj = -8.181575, rho = -0.191739 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 45 nu = 0.791421 obj = -10.145803, rho = -0.131582 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 50 nu = 0.680799 obj = -12.441398, rho = -0.079383 nSV = 71, nBSV = 64 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 38 nu = 0.588063 obj = -15.268084, rho = -0.147591 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 37 nu = 0.503488 obj = -18.473100, rho = -0.189666 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 36 nu = 0.428061 obj = -22.245396, rho = -0.265254 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 46 nu = 0.352780 obj = -26.627462, rho = -0.246054 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 41 nu = 0.303064 obj = -31.754113, rho = -0.250141 nSV = 32, nBSV = 28 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.251072 obj = -37.254805, rho = -0.260230 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.204100 obj = -43.422103, rho = -0.256646 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.161610 obj = -50.949785, rho = -0.260516 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 159 nu = 0.137478 obj = -59.979533, rho = -0.489329 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 81 nu = 0.112319 obj = -69.478880, rho = -0.556235 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.800000 obj = -0.786130, rho = 0.942084 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 52.3% (523/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -1.122062, rho = 0.916691 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 52.3% (523/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -1.595930, rho = 0.880164 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 52.3% (523/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -2.258215, rho = 0.827622 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 52.3% (523/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -3.170839, rho = 0.752044 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 52.3% (523/1000) (classification) * optimization finished, #iter = 42 nu = 0.800000 obj = -4.400754, rho = 0.643327 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 52.3% (523/1000) (classification) * optimization finished, #iter = 42 nu = 0.800000 obj = -5.998503, rho = 0.486944 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 66% (66/100) (classification) Accuracy = 59.4% (594/1000) (classification) * optimization finished, #iter = 41 nu = 0.800000 obj = -7.942085, rho = 0.261994 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 86% (86/100) (classification) Accuracy = 85.5% (855/1000) (classification) * optimization finished, #iter = 42 nu = 0.760000 obj = -10.068641, rho = 0.141883 nSV = 77, nBSV = 75 Total nSV = 77 Accuracy = 97% (97/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 52 nu = 0.673515 obj = -12.479158, rho = 0.157518 nSV = 71, nBSV = 65 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 38 nu = 0.576801 obj = -15.444075, rho = 0.110978 nSV = 59, nBSV = 56 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 26 nu = 0.499369 obj = -19.089276, rho = 0.082533 nSV = 50, nBSV = 48 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 51 nu = 0.435750 obj = -23.324815, rho = 0.094956 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 30 nu = 0.365821 obj = -28.482391, rho = 0.071710 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 36 nu = 0.315728 obj = -34.617170, rho = 0.096900 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 39 nu = 0.266484 obj = -41.764663, rho = 0.138423 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 60 nu = 0.228012 obj = -49.777988, rho = 0.152316 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 136 nu = 0.190494 obj = -58.559833, rho = 0.086915 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 97 nu = 0.153169 obj = -68.631431, rho = 0.170024 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 72 nu = 0.125193 obj = -81.657804, rho = 0.118984 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -0.929041, rho = 0.873154 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.316854, rho = 0.817538 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.853828, rho = 0.737537 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -2.583044, rho = 0.622460 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -3.542611, rho = 0.456928 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 55% (55/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.737973, rho = 0.218818 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 79% (79/100) (classification) Accuracy = 78.3% (783/1000) (classification) * optimization finished, #iter = 48 nu = 0.944493 obj = -6.078323, rho = -0.039240 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 97% (97/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 47 nu = 0.859341 obj = -7.517406, rho = -0.063085 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 46 nu = 0.743116 obj = -9.098466, rho = -0.008471 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 50 nu = 0.616995 obj = -10.902174, rho = -0.064488 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 33 nu = 0.520000 obj = -13.132549, rho = -0.128158 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 43 nu = 0.435568 obj = -15.648042, rho = -0.146172 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 72 nu = 0.360469 obj = -18.572121, rho = -0.191957 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 51 nu = 0.300913 obj = -22.063603, rho = -0.262043 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 45 nu = 0.247000 obj = -26.028241, rho = -0.239200 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 98 nu = 0.204749 obj = -30.288085, rho = -0.187754 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 98 nu = 0.163425 obj = -35.645150, rho = -0.202684 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 61 nu = 0.137285 obj = -41.612138, rho = -0.265450 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 86 nu = 0.108184 obj = -48.553222, rho = -0.346705 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 91 nu = 0.088683 obj = -57.457900, rho = -0.389329 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -0.893528, rho = 0.879331 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -1.268599, rho = 0.826424 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -1.790270, rho = 0.750320 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -2.503734, rho = 0.640847 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -3.453594, rho = 0.483377 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 55% (55/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -4.661792, rho = 0.256863 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 78% (78/100) (classification) Accuracy = 73.4% (734/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -6.082783, rho = 0.022720 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 95% (95/100) (classification) Accuracy = 90.7% (907/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -7.659503, rho = -0.032580 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 99% (99/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 48 nu = 0.740000 obj = -9.455573, rho = 0.001812 nSV = 76, nBSV = 72 Total nSV = 76 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 46 nu = 0.649288 obj = -11.523459, rho = -0.033093 nSV = 67, nBSV = 64 Total nSV = 67 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 40 nu = 0.551056 obj = -13.808134, rho = -0.096244 nSV = 56, nBSV = 54 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 49 nu = 0.450716 obj = -16.548684, rho = -0.114857 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 80 nu = 0.374821 obj = -19.839753, rho = -0.107354 nSV = 42, nBSV = 33 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 63 nu = 0.316353 obj = -23.991019, rho = -0.087749 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 70 nu = 0.271620 obj = -28.494870, rho = -0.039781 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 55 nu = 0.220953 obj = -33.760788, rho = -0.030568 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 70 nu = 0.180361 obj = -40.176567, rho = -0.029514 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 95 nu = 0.152807 obj = -47.756564, rho = 0.014188 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 54 nu = 0.127252 obj = -55.703699, rho = 0.177658 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 56 nu = 0.105434 obj = -63.882491, rho = 0.119779 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -0.952730, rho = 0.890936 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 47.1% (471/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.353256, rho = 0.843117 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 47.1% (471/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -1.911005, rho = 0.774331 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 47.1% (471/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -2.675252, rho = 0.675387 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 47.1% (471/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -3.695859, rho = 0.533060 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 47.2% (472/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -5.001060, rho = 0.328330 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 72% (72/100) (classification) Accuracy = 66.9% (669/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -6.541484, rho = 0.033837 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 95% (95/100) (classification) Accuracy = 91.5% (915/1000) (classification) * optimization finished, #iter = 52 nu = 0.889693 obj = -8.271845, rho = -0.011570 nSV = 90, nBSV = 86 Total nSV = 90 Accuracy = 98% (98/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 41 nu = 0.780000 obj = -10.416518, rho = 0.041453 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 99% (99/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 41 nu = 0.694292 obj = -12.981449, rho = -0.057548 nSV = 72, nBSV = 68 Total nSV = 72 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 44 nu = 0.609553 obj = -15.994606, rho = -0.005789 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 46 nu = 0.527049 obj = -19.506261, rho = 0.070410 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 37 nu = 0.445675 obj = -23.472308, rho = 0.087715 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 66 nu = 0.373730 obj = -28.122415, rho = 0.055103 nSV = 42, nBSV = 34 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 29 nu = 0.319053 obj = -33.739731, rho = 0.020021 nSV = 33, nBSV = 29 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 73 nu = 0.261580 obj = -39.903072, rho = -0.010722 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 81 nu = 0.218365 obj = -46.802148, rho = -0.057269 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.177773 obj = -54.906128, rho = -0.081862 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 231 nu = 0.141300 obj = -64.966746, rho = -0.092580 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 85 nu = 0.115518 obj = -78.035559, rho = -0.092592 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -0.882943, rho = 0.921731 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -1.259311, rho = 0.887414 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -1.789197, rho = 0.838051 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -2.527613, rho = 0.767044 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -3.540546, rho = 0.664904 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.7% (497/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -4.895711, rho = 0.517982 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -6.634229, rho = 0.306641 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 73% (73/100) (classification) Accuracy = 72.4% (724/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -8.698785, rho = 0.002638 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 93% (93/100) (classification) Accuracy = 92.9% (929/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -11.059614, rho = 0.006018 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 97% (97/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 46 nu = 0.744163 obj = -13.801607, rho = -0.091111 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 63 nu = 0.648169 obj = -17.001015, rho = -0.060685 nSV = 68, nBSV = 62 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 48 nu = 0.560876 obj = -20.803775, rho = -0.004909 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 53 nu = 0.471062 obj = -25.320092, rho = 0.020010 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 25 nu = 0.400691 obj = -30.688849, rho = 0.117953 nSV = 42, nBSV = 39 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 36 nu = 0.340834 obj = -37.075646, rho = 0.083331 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 53 nu = 0.283748 obj = -44.473701, rho = 0.051946 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 82 nu = 0.238520 obj = -53.380022, rho = -0.040002 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.203202 obj = -63.516925, rho = -0.070948 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 142 nu = 0.163504 obj = -75.638972, rho = -0.026375 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.136954 obj = -90.883883, rho = 0.034738 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.947312, rho = -0.886625 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.342046, rho = -0.836915 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.887809, rho = -0.765411 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.627256, rho = -0.662556 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.596549, rho = -0.514603 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 73% (73/100) (classification) Accuracy = 65.1% (651/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.795574, rho = -0.301781 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 86% (86/100) (classification) Accuracy = 90.6% (906/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -6.192604, rho = -0.248110 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 89% (89/100) (classification) Accuracy = 94.8% (948/1000) (classification) * optimization finished, #iter = 42 nu = 0.822614 obj = -7.916436, rho = -0.260496 nSV = 84, nBSV = 82 Total nSV = 84 Accuracy = 93% (93/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 51 nu = 0.733700 obj = -10.104346, rho = -0.267356 nSV = 75, nBSV = 71 Total nSV = 75 Accuracy = 94% (94/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 55 nu = 0.661548 obj = -12.888955, rho = -0.222473 nSV = 68, nBSV = 64 Total nSV = 68 Accuracy = 95% (95/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 46 nu = 0.588053 obj = -16.243047, rho = -0.162790 nSV = 63, nBSV = 57 Total nSV = 63 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 34 nu = 0.524383 obj = -20.310808, rho = -0.148485 nSV = 54, nBSV = 51 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 49 nu = 0.462087 obj = -24.837882, rho = -0.056306 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 42 nu = 0.393269 obj = -30.218777, rho = 0.023570 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 71 nu = 0.331017 obj = -36.457592, rho = 0.086067 nSV = 38, nBSV = 29 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 81 nu = 0.282864 obj = -43.739272, rho = 0.189390 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 95 nu = 0.236733 obj = -52.020898, rho = 0.171900 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 81 nu = 0.194128 obj = -61.702892, rho = 0.191739 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 279 nu = 0.161316 obj = -72.986355, rho = 0.231774 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.133746 obj = -85.908689, rho = 0.343651 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -0.939296, rho = -0.932011 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 47.9% (479/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.338073, rho = -0.902202 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 47.9% (479/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.897734, rho = -0.859322 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 47.9% (479/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.673892, rho = -0.797642 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 47.9% (479/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.730588, rho = -0.708918 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 47.9% (479/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -5.126923, rho = -0.581292 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -6.879593, rho = -0.397710 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 87% (87/100) (classification) Accuracy = 82.3% (823/1000) (classification) * optimization finished, #iter = 48 nu = 0.952568 obj = -8.872293, rho = -0.157394 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 49 nu = 0.847160 obj = -11.059984, rho = -0.180684 nSV = 87, nBSV = 83 Total nSV = 87 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 47 nu = 0.731957 obj = -13.672489, rho = -0.150510 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 98% (98/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 42 nu = 0.643904 obj = -16.840728, rho = -0.121454 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 99.3% (993/1000) (classification) * optimization finished, #iter = 63 nu = 0.546632 obj = -20.612579, rho = -0.159676 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 99.4% (994/1000) (classification) * optimization finished, #iter = 87 nu = 0.465291 obj = -25.165038, rho = -0.275177 nSV = 51, nBSV = 41 Total nSV = 51 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 79 nu = 0.392304 obj = -30.978395, rho = -0.317108 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 43 nu = 0.336531 obj = -38.268140, rho = -0.297307 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 66 nu = 0.282682 obj = -47.153923, rho = -0.301355 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 52 nu = 0.247671 obj = -58.452093, rho = -0.218617 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 51 nu = 0.210185 obj = -72.561566, rho = -0.140846 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 73 nu = 0.185229 obj = -90.217611, rho = -0.043248 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 96 nu = 0.158664 obj = -110.002353, rho = -0.026296 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.955278, rho = 0.892888 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.358528, rho = 0.845924 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.921912, rho = 0.778370 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.697821, rho = 0.681196 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.742557, rho = 0.541417 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -5.097685, rho = 0.340351 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 72% (72/100) (classification) Accuracy = 68.7% (687/1000) (classification) * optimization finished, #iter = 49 nu = 0.973772 obj = -6.742036, rho = 0.074819 nSV = 98, nBSV = 96 Total nSV = 98 Accuracy = 95% (95/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 47 nu = 0.908651 obj = -8.678056, rho = 0.133952 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 95% (95/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 48 nu = 0.832649 obj = -10.921085, rho = 0.090798 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.719625 obj = -13.611928, rho = 0.057709 nSV = 73, nBSV = 70 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 38 nu = 0.624570 obj = -16.927928, rho = 0.021454 nSV = 65, nBSV = 60 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 37 nu = 0.550689 obj = -20.898662, rho = 0.028090 nSV = 57, nBSV = 54 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 43 nu = 0.477558 obj = -25.622860, rho = 0.039764 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 42 nu = 0.407593 obj = -31.161446, rho = 0.056911 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 72 nu = 0.343735 obj = -37.488304, rho = 0.086971 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.290518 obj = -45.123266, rho = 0.100722 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 59 nu = 0.242507 obj = -53.982775, rho = 0.044094 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 89 nu = 0.202387 obj = -64.874169, rho = -0.106051 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 143 nu = 0.168861 obj = -77.355430, rho = -0.076062 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 191 nu = 0.134585 obj = -93.376160, rho = -0.069346 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.957239, rho = -0.919965 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.362585, rho = -0.884873 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.930307, rho = -0.834396 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.715190, rho = -0.761787 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.778495, rho = -0.657342 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -5.172047, rho = -0.507104 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 62% (62/100) (classification) Accuracy = 61.6% (616/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -6.895278, rho = -0.290993 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 89% (89/100) (classification) Accuracy = 93.6% (936/1000) (classification) * optimization finished, #iter = 51 nu = 0.940000 obj = -8.853188, rho = -0.121110 nSV = 95, nBSV = 92 Total nSV = 95 Accuracy = 96% (96/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 56 nu = 0.851304 obj = -11.074874, rho = -0.145114 nSV = 88, nBSV = 84 Total nSV = 88 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.740000 obj = -13.691641, rho = -0.128570 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 57 nu = 0.645235 obj = -16.828992, rho = -0.085498 nSV = 66, nBSV = 61 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 34 nu = 0.550157 obj = -20.527465, rho = -0.042903 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 44 nu = 0.473103 obj = -24.889310, rho = -0.032649 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 72 nu = 0.396721 obj = -29.859611, rho = 0.003040 nSV = 45, nBSV = 36 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 74 nu = 0.326600 obj = -35.886884, rho = 0.010966 nSV = 37, nBSV = 28 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 89 nu = 0.274350 obj = -43.522390, rho = 0.089927 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 70 nu = 0.227922 obj = -53.062743, rho = 0.106982 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 71 nu = 0.190329 obj = -65.290069, rho = 0.121311 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.167655 obj = -80.530604, rho = 0.158189 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 68 nu = 0.143140 obj = -98.284085, rho = -0.022916 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -0.898324, rho = 0.919502 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -1.278523, rho = 0.884208 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.810804, rho = 0.833438 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -2.546222, rho = 0.760410 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -3.541507, rho = 0.655361 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -4.843696, rho = 0.504254 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 57% (57/100) (classification) Accuracy = 52.8% (528/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -6.448920, rho = 0.286895 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 81% (81/100) (classification) Accuracy = 80.3% (803/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -8.217808, rho = 0.091258 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 98% (98/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 49 nu = 0.809138 obj = -10.117590, rho = 0.029182 nSV = 83, nBSV = 78 Total nSV = 83 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 41 nu = 0.708801 obj = -12.163033, rho = -0.076730 nSV = 74, nBSV = 70 Total nSV = 74 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 51 nu = 0.583713 obj = -14.322042, rho = -0.042213 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 39 nu = 0.481914 obj = -16.830440, rho = -0.022856 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.392560 obj = -19.721631, rho = 0.002317 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 63 nu = 0.321646 obj = -23.036328, rho = -0.070502 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 60 nu = 0.270988 obj = -26.863895, rho = -0.116725 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 66 nu = 0.221018 obj = -30.394706, rho = -0.076451 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.*.* optimization finished, #iter = 146 nu = 0.178004 obj = -33.686025, rho = -0.005042 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 158 nu = 0.134146 obj = -36.499389, rho = -0.004611 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.102954 obj = -39.761084, rho = -0.025732 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ...*.....* optimization finished, #iter = 857 nu = 0.077729 obj = -42.246806, rho = -0.084682 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -0.900079, rho = -0.941429 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.282154, rho = -0.915749 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.818318, rho = -0.878809 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -2.561768, rho = -0.825673 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -3.573674, rho = -0.749240 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -4.910254, rho = -0.639294 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 57% (57/100) (classification) Accuracy = 52.6% (526/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -6.586638, rho = -0.481143 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 80% (80/100) (classification) Accuracy = 80.4% (804/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -8.560610, rho = -0.346253 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 97% (97/100) (classification) Accuracy = 94.5% (945/1000) (classification) * optimization finished, #iter = 48 nu = 0.815834 obj = -10.816699, rho = -0.224305 nSV = 84, nBSV = 80 Total nSV = 84 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 50 nu = 0.719114 obj = -13.424783, rho = -0.181780 nSV = 75, nBSV = 70 Total nSV = 75 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 42 nu = 0.631516 obj = -16.560535, rho = -0.201370 nSV = 65, nBSV = 60 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 40 nu = 0.545183 obj = -20.032098, rho = -0.152589 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 53 nu = 0.462744 obj = -23.897702, rho = -0.078640 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 37 nu = 0.390882 obj = -27.998882, rho = -0.120797 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 43 nu = 0.324546 obj = -32.468567, rho = -0.191401 nSV = 35, nBSV = 30 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 86 nu = 0.259512 obj = -37.156148, rho = -0.149504 nSV = 31, nBSV = 21 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 86 nu = 0.213967 obj = -42.284205, rho = -0.165351 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..**.* optimization finished, #iter = 337 nu = 0.167564 obj = -46.769426, rho = -0.160945 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) . WARNING: using -h 0 may be faster * optimization finished, #iter = 190 nu = 0.128158 obj = -52.025667, rho = -0.159707 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*...* optimization finished, #iter = 462 nu = 0.097898 obj = -57.724012, rho = -0.192048 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -0.880797, rho = -0.934458 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 53.4% (534/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -1.254871, rho = -0.905722 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 53.4% (534/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -1.780010, rho = -0.864385 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 53.4% (534/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -2.508603, rho = -0.804925 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 53.4% (534/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -3.501211, rho = -0.719395 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 53.4% (534/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -4.814321, rho = -0.596363 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 56% (56/100) (classification) Accuracy = 54.2% (542/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -6.465822, rho = -0.419389 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 79% (79/100) (classification) Accuracy = 78.5% (785/1000) (classification) * optimization finished, #iter = 49 nu = 0.882119 obj = -8.359004, rho = -0.204736 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 95% (95/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 46 nu = 0.795867 obj = -10.526324, rho = -0.106789 nSV = 81, nBSV = 77 Total nSV = 81 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 46 nu = 0.693050 obj = -13.119371, rho = -0.174548 nSV = 72, nBSV = 68 Total nSV = 72 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 38 nu = 0.608020 obj = -16.194085, rho = -0.203940 nSV = 63, nBSV = 60 Total nSV = 63 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 29 nu = 0.540000 obj = -19.795930, rho = -0.116463 nSV = 55, nBSV = 53 Total nSV = 55 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 37 nu = 0.450346 obj = -23.845866, rho = -0.185105 nSV = 47, nBSV = 43 Total nSV = 47 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 39 nu = 0.380966 obj = -28.817381, rho = -0.224190 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 49 nu = 0.320331 obj = -34.569513, rho = -0.196835 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 64 nu = 0.266776 obj = -41.350509, rho = -0.206971 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.218626 obj = -49.370723, rho = -0.254962 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.177437 obj = -60.122406, rho = -0.271765 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 179 nu = 0.151566 obj = -74.358148, rho = -0.292864 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 219 nu = 0.127734 obj = -92.110398, rho = -0.323549 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -0.921010, rho = 0.899436 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.312850, rho = 0.855344 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.863688, rho = 0.791920 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.629545, rho = 0.700687 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.676371, rho = 0.569454 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -5.068745, rho = 0.380681 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 58% (58/100) (classification) Accuracy = 56.2% (562/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.836896, rho = 0.109140 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 88% (88/100) (classification) Accuracy = 86.6% (866/1000) (classification) * optimization finished, #iter = 48 nu = 0.912063 obj = -8.915511, rho = -0.121596 nSV = 93, nBSV = 90 Total nSV = 93 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 49 nu = 0.848180 obj = -11.325294, rho = -0.041170 nSV = 87, nBSV = 82 Total nSV = 87 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 43 nu = 0.760000 obj = -14.079227, rho = -0.122101 nSV = 77, nBSV = 75 Total nSV = 77 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 39 nu = 0.653977 obj = -17.352039, rho = -0.134151 nSV = 67, nBSV = 63 Total nSV = 67 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.556053 obj = -21.267150, rho = -0.172146 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.478571 obj = -26.146583, rho = -0.187169 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 43 nu = 0.413216 obj = -32.030064, rho = -0.119280 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.350813 obj = -38.778118, rho = -0.008930 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 66 nu = 0.292692 obj = -47.219122, rho = 0.057686 nSV = 34, nBSV = 25 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 87 nu = 0.247982 obj = -57.732752, rho = 0.092328 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 72 nu = 0.209237 obj = -70.658033, rho = 0.070160 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 150 nu = 0.180529 obj = -86.007374, rho = -0.070492 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *..* optimization finished, #iter = 213 nu = 0.149779 obj = -105.514138, rho = -0.063830 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -0.837118, rho = -0.943279 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -1.189721, rho = -0.918409 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -1.681493, rho = -0.882636 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -2.356958, rho = -0.831177 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -3.262524, rho = -0.757157 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -4.428452, rho = -0.650682 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 73% (73/100) (classification) Accuracy = 66.9% (669/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -5.822768, rho = -0.497524 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 88% (88/100) (classification) Accuracy = 86.4% (864/1000) (classification) * optimization finished, #iter = 43 nu = 0.788684 obj = -7.385683, rho = -0.395343 nSV = 80, nBSV = 76 Total nSV = 80 Accuracy = 96% (96/100) (classification) Accuracy = 92.4% (924/1000) (classification) * optimization finished, #iter = 43 nu = 0.700613 obj = -9.295667, rho = -0.312739 nSV = 72, nBSV = 69 Total nSV = 72 Accuracy = 98% (98/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 39 nu = 0.615046 obj = -11.601326, rho = -0.235668 nSV = 63, nBSV = 60 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 49 nu = 0.544837 obj = -14.265995, rho = -0.237682 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 40 nu = 0.466258 obj = -17.379771, rho = -0.188188 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 30 nu = 0.399860 obj = -21.123063, rho = -0.106465 nSV = 41, nBSV = 38 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 40 nu = 0.336979 obj = -25.506411, rho = -0.145083 nSV = 35, nBSV = 31 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 64 nu = 0.286029 obj = -30.586688, rho = -0.111859 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 62 nu = 0.236167 obj = -36.336515, rho = -0.180190 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 42 nu = 0.199384 obj = -43.445116, rho = -0.228392 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 182 nu = 0.164260 obj = -51.319076, rho = -0.380907 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 89 nu = 0.132732 obj = -60.793081, rho = -0.391971 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.109334 obj = -72.525043, rho = -0.399724 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -0.877943, rho = -0.941260 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 47.3% (473/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -1.248965, rho = -0.915505 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 47.3% (473/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -1.767789, rho = -0.878458 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 47.3% (473/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -2.483316, rho = -0.825168 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 47.3% (473/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -3.448890, rho = -0.748513 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 47.3% (473/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -4.706062, rho = -0.638248 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 64% (64/100) (classification) Accuracy = 59.4% (594/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -6.241818, rho = -0.479638 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 94% (94/100) (classification) Accuracy = 88.4% (884/1000) (classification) * optimization finished, #iter = 46 nu = 0.857819 obj = -7.982330, rho = -0.339454 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 95% (95/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 39 nu = 0.780000 obj = -9.926005, rho = -0.242536 nSV = 78, nBSV = 78 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 52 nu = 0.681255 obj = -12.025180, rho = -0.263621 nSV = 70, nBSV = 65 Total nSV = 70 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 35 nu = 0.565046 obj = -14.542576, rho = -0.262518 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 43 nu = 0.483110 obj = -17.404994, rho = -0.393813 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 52 nu = 0.399914 obj = -20.750338, rho = -0.437427 nSV = 44, nBSV = 36 Total nSV = 44 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 74 nu = 0.327199 obj = -24.950746, rho = -0.457631 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.271891 obj = -30.257378, rho = -0.450201 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 164 nu = 0.222643 obj = -37.291132, rho = -0.439082 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 37 nu = 0.193532 obj = -46.480452, rho = -0.485259 nSV = 22, nBSV = 18 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 66 nu = 0.171960 obj = -57.402823, rho = -0.413825 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.143757 obj = -69.957357, rho = -0.385108 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.124656 obj = -85.760754, rho = -0.364702 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -0.843558, rho = -0.950245 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -1.203045, rho = -0.928429 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -1.709063, rho = -0.897049 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -2.414004, rho = -0.851910 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -3.380559, rho = -0.786980 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -4.672683, rho = -0.693582 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -6.328115, rho = -0.559233 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 69% (69/100) (classification) Accuracy = 66.6% (666/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -8.288874, rho = -0.365978 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 95% (95/100) (classification) Accuracy = 91.5% (915/1000) (classification) * optimization finished, #iter = 43 nu = 0.780941 obj = -10.453293, rho = -0.250221 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 99% (99/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 40 nu = 0.694776 obj = -13.067108, rho = -0.188717 nSV = 72, nBSV = 68 Total nSV = 72 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 36 nu = 0.620000 obj = -16.053699, rho = -0.135395 nSV = 63, nBSV = 61 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 63 nu = 0.523024 obj = -19.425510, rho = -0.149631 nSV = 56, nBSV = 49 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 49 nu = 0.444284 obj = -23.462438, rho = -0.190228 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 40 nu = 0.378017 obj = -28.337363, rho = -0.287253 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.311940 obj = -33.878240, rho = -0.289459 nSV = 37, nBSV = 28 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 39 nu = 0.260673 obj = -40.888282, rho = -0.276249 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.218234 obj = -49.216618, rho = -0.399748 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 59 nu = 0.182152 obj = -58.998634, rho = -0.567085 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 62 nu = 0.155000 obj = -70.697448, rho = -0.569335 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 138 nu = 0.125725 obj = -84.911595, rho = -0.587408 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.971665, rho = -0.034893 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.379820, rho = -0.050193 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.947826, rho = -0.072199 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.725339, rho = -0.103855 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.761953, rho = -0.149391 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -5.083815, rho = -0.214891 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -6.646803, rho = -0.243706 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 46 nu = 0.915207 obj = -8.433919, rho = -0.239469 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 55 nu = 0.817165 obj = -10.458853, rho = -0.202435 nSV = 83, nBSV = 79 Total nSV = 83 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 47 nu = 0.705189 obj = -12.824488, rho = -0.148819 nSV = 72, nBSV = 68 Total nSV = 72 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 46 nu = 0.588111 obj = -15.743604, rho = -0.125256 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 35 nu = 0.508115 obj = -19.355446, rho = -0.101190 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 32 nu = 0.440202 obj = -23.665138, rho = -0.047092 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 59 nu = 0.377116 obj = -28.826439, rho = -0.060358 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 26 nu = 0.320060 obj = -34.927240, rho = -0.089833 nSV = 34, nBSV = 31 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 48 nu = 0.280882 obj = -41.618730, rho = -0.086175 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 56 nu = 0.226672 obj = -48.519336, rho = -0.137814 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 90 nu = 0.182664 obj = -56.986034, rho = -0.177604 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 76 nu = 0.154141 obj = -67.201238, rho = -0.119410 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 63 nu = 0.126497 obj = -76.490376, rho = -0.143743 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -0.780790, rho = -0.950528 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -1.111013, rho = -0.928837 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -1.573068, rho = -0.897635 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -2.210910, rho = -0.852753 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -3.072959, rho = -0.788193 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -4.198228, rho = -0.695326 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 61% (61/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -5.579448, rho = -0.561742 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 78% (78/100) (classification) Accuracy = 70% (700/1000) (classification) * optimization finished, #iter = 40 nu = 0.780000 obj = -7.084490, rho = -0.392471 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 95% (95/100) (classification) Accuracy = 93.8% (938/1000) (classification) * optimization finished, #iter = 48 nu = 0.705322 obj = -8.595173, rho = -0.267463 nSV = 73, nBSV = 67 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 42 nu = 0.591323 obj = -10.259283, rho = -0.206339 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.500830 obj = -12.071300, rho = -0.116099 nSV = 53, nBSV = 46 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 49 nu = 0.400416 obj = -14.182011, rho = -0.124107 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 33 nu = 0.329950 obj = -16.686902, rho = -0.151731 nSV = 35, nBSV = 30 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 59 nu = 0.267608 obj = -19.703166, rho = -0.102423 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.217018 obj = -23.487917, rho = -0.106592 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 60 nu = 0.178512 obj = -28.327650, rho = -0.097260 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 57 nu = 0.147888 obj = -34.394248, rho = -0.117084 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 71 nu = 0.123838 obj = -42.137893, rho = -0.158460 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 65 nu = 0.105610 obj = -52.185731, rho = -0.143924 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 50 nu = 0.097746 obj = -63.520068, rho = -0.289007 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -0.880155, rho = -0.947031 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -1.253542, rho = -0.923807 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -1.777260, rho = -0.890400 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -2.502913, rho = -0.842346 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -3.489439, rho = -0.773223 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -4.789963, rho = -0.673793 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 58% (58/100) (classification) Accuracy = 51.8% (518/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -6.415422, rho = -0.530768 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 77% (77/100) (classification) Accuracy = 76.3% (763/1000) (classification) * optimization finished, #iter = 45 nu = 0.868821 obj = -8.279919, rho = -0.393883 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 91% (91/100) (classification) Accuracy = 92.8% (928/1000) (classification) * optimization finished, #iter = 46 nu = 0.801103 obj = -10.396195, rho = -0.312776 nSV = 83, nBSV = 79 Total nSV = 83 Accuracy = 95% (95/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 41 nu = 0.697596 obj = -12.690808, rho = -0.237866 nSV = 71, nBSV = 68 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 53 nu = 0.604769 obj = -15.397822, rho = -0.178384 nSV = 63, nBSV = 57 Total nSV = 63 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 38 nu = 0.513198 obj = -18.468822, rho = -0.135055 nSV = 53, nBSV = 50 Total nSV = 53 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 175 nu = 0.426440 obj = -21.874685, rho = -0.066328 nSV = 47, nBSV = 37 Total nSV = 47 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.343229 obj = -26.177693, rho = -0.036304 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 153 nu = 0.285539 obj = -31.760154, rho = -0.031344 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.243704 obj = -38.468755, rho = -0.010067 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) *..* optimization finished, #iter = 287 nu = 0.202155 obj = -46.547360, rho = 0.036236 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.170836 obj = -57.031218, rho = 0.115008 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 205 nu = 0.147856 obj = -69.541057, rho = 0.171433 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) ..*..* optimization finished, #iter = 462 nu = 0.122889 obj = -83.925114, rho = 0.171822 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.947092, rho = 0.864706 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.341589, rho = 0.805386 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.886864, rho = 0.720057 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.625300, rho = 0.597317 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.592502, rho = 0.420760 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 54% (54/100) (classification) Accuracy = 54.5% (545/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.787201, rho = 0.166792 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 89% (89/100) (classification) Accuracy = 88% (880/1000) (classification) * optimization finished, #iter = 51 nu = 0.956254 obj = -6.112864, rho = -0.111679 nSV = 97, nBSV = 94 Total nSV = 97 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 55 nu = 0.848423 obj = -7.566553, rho = -0.061049 nSV = 86, nBSV = 82 Total nSV = 86 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 44 nu = 0.733530 obj = -9.285254, rho = -0.058848 nSV = 76, nBSV = 72 Total nSV = 76 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.635244 obj = -11.284750, rho = -0.037478 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 33 nu = 0.540000 obj = -13.569812, rho = -0.001703 nSV = 56, nBSV = 53 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 63 nu = 0.455510 obj = -16.098487, rho = 0.064293 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 31 nu = 0.372820 obj = -19.062505, rho = 0.095492 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.314452 obj = -22.387485, rho = 0.027258 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 96 nu = 0.249160 obj = -26.266665, rho = -0.034776 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 33 nu = 0.204768 obj = -31.044943, rho = -0.124329 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 42 nu = 0.170510 obj = -36.766042, rho = -0.230032 nSV = 19, nBSV = 14 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 64 nu = 0.138557 obj = -43.160510, rho = -0.240070 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 76 nu = 0.116790 obj = -50.160586, rho = -0.098169 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 87 nu = 0.094144 obj = -57.128686, rho = -0.135766 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -0.928878, rho = -0.890993 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.316517, rho = -0.843198 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.853130, rho = -0.774448 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.581600, rho = -0.675555 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.539624, rho = -0.533303 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 55% (55/100) (classification) Accuracy = 54.4% (544/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.731793, rho = -0.328679 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 87% (87/100) (classification) Accuracy = 84% (840/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -6.093349, rho = -0.151561 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 96% (96/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 45 nu = 0.848567 obj = -7.628670, rho = 0.014185 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 43 nu = 0.750636 obj = -9.343206, rho = 0.015380 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 53 nu = 0.642278 obj = -11.182058, rho = 0.056740 nSV = 66, nBSV = 61 Total nSV = 66 Accuracy = 96% (96/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 38 nu = 0.541496 obj = -13.284764, rho = 0.089326 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 96% (96/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 39 nu = 0.440710 obj = -15.732433, rho = 0.095671 nSV = 47, nBSV = 43 Total nSV = 47 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 49 nu = 0.360027 obj = -18.754179, rho = 0.069865 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 96% (96/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 42 nu = 0.297735 obj = -22.454169, rho = 0.071348 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 31 nu = 0.246063 obj = -27.200847, rho = 0.062718 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 54 nu = 0.209253 obj = -33.048035, rho = 0.141719 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 66 nu = 0.173483 obj = -40.155710, rho = 0.125625 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 90 nu = 0.144689 obj = -49.401525, rho = 0.252229 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 52 nu = 0.122569 obj = -61.499544, rho = 0.271078 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.105988 obj = -77.040578, rho = 0.212108 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -0.876751, rho = 0.899711 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -1.246501, rho = 0.855739 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -1.762690, rho = 0.792488 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -2.472765, rho = 0.701505 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -3.427058, rho = 0.570630 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.6% (496/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -4.660890, rho = 0.382372 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 64% (64/100) (classification) Accuracy = 59.9% (599/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -6.148351, rho = 0.111573 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 91% (91/100) (classification) Accuracy = 86.1% (861/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -7.819657, rho = -0.042414 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 95% (95/100) (classification) Accuracy = 94.8% (948/1000) (classification) * optimization finished, #iter = 41 nu = 0.755387 obj = -9.717603, rho = -0.059593 nSV = 76, nBSV = 74 Total nSV = 76 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 34 nu = 0.659264 obj = -11.883435, rho = -0.007560 nSV = 66, nBSV = 64 Total nSV = 66 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 36 nu = 0.563138 obj = -14.334325, rho = -0.033191 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 32 nu = 0.476932 obj = -17.162129, rho = -0.056769 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 52 nu = 0.397153 obj = -20.526698, rho = -0.063345 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 48 nu = 0.329504 obj = -24.441768, rho = -0.104745 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 48 nu = 0.269755 obj = -29.187356, rho = -0.105400 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.226054 obj = -34.929636, rho = -0.045834 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 28 nu = 0.184963 obj = -42.114580, rho = -0.042170 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 77 nu = 0.153245 obj = -51.143361, rho = 0.003163 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 66 nu = 0.130458 obj = -62.877815, rho = 0.063140 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 65 nu = 0.112061 obj = -76.193762, rho = 0.069318 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -0.936711, rho = 0.880084 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.332723, rho = 0.827507 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.886663, rho = 0.751877 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -2.650984, rho = 0.643087 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -3.683188, rho = 0.486599 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 51% (510/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -5.028846, rho = 0.261499 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 64% (64/100) (classification) Accuracy = 68.4% (684/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -6.676657, rho = -0.062297 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 94% (94/100) (classification) Accuracy = 94.5% (945/1000) (classification) * optimization finished, #iter = 46 nu = 0.903783 obj = -8.566348, rho = -0.153099 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 96% (96/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 46 nu = 0.828782 obj = -10.701867, rho = -0.148336 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 96% (96/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 44 nu = 0.729797 obj = -13.130215, rho = -0.183562 nSV = 74, nBSV = 70 Total nSV = 74 Accuracy = 96% (96/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 48 nu = 0.634412 obj = -15.854085, rho = -0.209266 nSV = 66, nBSV = 61 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 62 nu = 0.518974 obj = -18.952613, rho = -0.224918 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 35 nu = 0.435499 obj = -22.932768, rho = -0.308770 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 64 nu = 0.369575 obj = -27.478641, rho = -0.285474 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 46 nu = 0.309284 obj = -32.478193, rho = -0.245630 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 59 nu = 0.256571 obj = -38.189838, rho = -0.246428 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.212368 obj = -44.136692, rho = -0.361806 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) .* optimization finished, #iter = 164 nu = 0.169342 obj = -50.979701, rho = -0.383715 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) .* optimization finished, #iter = 155 nu = 0.139242 obj = -58.444871, rho = -0.459630 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 168 nu = 0.109390 obj = -65.568548, rho = -0.501805 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.963505, rho = -0.016612 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.362936, rho = -0.023895 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.912890, rho = -0.034372 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.653053, rho = -0.049442 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.612383, rho = -0.071120 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -4.774333, rho = -0.102302 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 47 nu = 0.934462 obj = -6.107881, rho = -0.139249 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 96% (96/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 45 nu = 0.844039 obj = -7.653092, rho = -0.054926 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 44 nu = 0.745279 obj = -9.425069, rho = -0.001125 nSV = 76, nBSV = 72 Total nSV = 76 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 63 nu = 0.640979 obj = -11.404858, rho = -0.051697 nSV = 67, nBSV = 62 Total nSV = 67 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 37 nu = 0.534743 obj = -13.823681, rho = -0.054206 nSV = 55, nBSV = 52 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 49 nu = 0.455641 obj = -16.697880, rho = -0.047898 nSV = 49, nBSV = 41 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 38 nu = 0.380637 obj = -20.183818, rho = -0.048720 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 64 nu = 0.326436 obj = -24.034413, rho = -0.010645 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 72 nu = 0.263444 obj = -28.637449, rho = 0.062190 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 52 nu = 0.220349 obj = -34.220996, rho = 0.146813 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *..* optimization finished, #iter = 203 nu = 0.183565 obj = -40.787562, rho = 0.218685 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.153870 obj = -48.486767, rho = 0.209750 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 56 nu = 0.123973 obj = -58.091817, rho = 0.226184 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.103650 obj = -69.975861, rho = 0.292737 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.966891, rho = -0.004024 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.369944, rho = -0.005789 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.927389, rho = -0.008327 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.683053, rho = -0.011978 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.674457, rho = -0.017230 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -4.902773, rho = -0.024784 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 48 nu = 0.945937 obj = -6.312233, rho = -0.028563 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 96% (96/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 47 nu = 0.855683 obj = -7.979172, rho = -0.021904 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 42 nu = 0.780000 obj = -9.960451, rho = -0.071358 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 41 nu = 0.672407 obj = -12.193119, rho = -0.065701 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 37 nu = 0.570898 obj = -14.812089, rho = -0.027290 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 95% (95/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 32 nu = 0.476169 obj = -18.122417, rho = -0.086403 nSV = 49, nBSV = 46 Total nSV = 49 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 51 nu = 0.407647 obj = -22.159636, rho = -0.096328 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 45 nu = 0.352204 obj = -26.987359, rho = -0.082916 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 43 nu = 0.303077 obj = -32.399482, rho = -0.197928 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 77 nu = 0.247158 obj = -38.733064, rho = -0.142863 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 49 nu = 0.207832 obj = -47.003675, rho = -0.204734 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 54 nu = 0.172984 obj = -56.873638, rho = -0.260664 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 72 nu = 0.150210 obj = -68.121228, rho = -0.187301 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 172 nu = 0.127345 obj = -78.654614, rho = -0.262010 nSV = 19, nBSV = 8 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.949947, rho = -0.900229 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.347497, rho = -0.856484 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.899088, rho = -0.793559 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.650595, rho = -0.703045 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.644840, rho = -0.572846 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 54% (540/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.895496, rho = -0.385560 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 84% (84/100) (classification) Accuracy = 83.9% (839/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -6.323056, rho = -0.126608 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 99% (99/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 47 nu = 0.881814 obj = -7.846910, rho = -0.075155 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 68 nu = 0.774281 obj = -9.510207, rho = -0.095614 nSV = 81, nBSV = 73 Total nSV = 81 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 64 nu = 0.650094 obj = -11.411708, rho = -0.065542 nSV = 68, nBSV = 62 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 52 nu = 0.539526 obj = -13.672119, rho = -0.058481 nSV = 57, nBSV = 50 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 56 nu = 0.444339 obj = -16.483065, rho = -0.108290 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 39 nu = 0.378110 obj = -19.998684, rho = -0.072748 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 38 nu = 0.313543 obj = -24.148380, rho = -0.055729 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 35 nu = 0.266155 obj = -29.404215, rho = -0.166820 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 76 nu = 0.226122 obj = -35.506140, rho = -0.137601 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 42 nu = 0.192739 obj = -42.623344, rho = -0.159065 nSV = 22, nBSV = 17 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 46 nu = 0.163646 obj = -50.514154, rho = -0.243604 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 64 nu = 0.134559 obj = -59.194401, rho = -0.433605 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 91 nu = 0.110689 obj = -67.979693, rho = -0.413395 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -0.838189, rho = 0.921429 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -1.191936, rho = 0.886979 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -1.686077, rho = 0.837425 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -2.366442, rho = 0.766144 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -3.282146, rho = 0.663609 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -4.469053, rho = 0.516119 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 62% (62/100) (classification) Accuracy = 54.1% (541/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -5.906778, rho = 0.303961 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 92% (92/100) (classification) Accuracy = 83.2% (832/1000) (classification) * optimization finished, #iter = 44 nu = 0.814978 obj = -7.478887, rho = 0.130952 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 100% (100/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 43 nu = 0.733752 obj = -9.232791, rho = 0.100942 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 49 nu = 0.620000 obj = -11.224332, rho = 0.062686 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 82 nu = 0.524849 obj = -13.663945, rho = 0.071935 nSV = 56, nBSV = 49 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 49 nu = 0.445040 obj = -16.654942, rho = 0.117694 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 32 nu = 0.373581 obj = -20.370548, rho = 0.159747 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 35 nu = 0.320000 obj = -24.981003, rho = 0.175466 nSV = 34, nBSV = 31 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 42 nu = 0.269685 obj = -30.380930, rho = 0.175216 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 34 nu = 0.224279 obj = -37.534368, rho = 0.115781 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 54 nu = 0.196372 obj = -46.769973, rho = 0.174642 nSV = 22, nBSV = 17 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 71 nu = 0.174676 obj = -57.308137, rho = 0.175825 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.149785 obj = -69.460636, rho = 0.145997 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 67 nu = 0.124975 obj = -83.361349, rho = 0.116005 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -0.931792, rho = -0.925485 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 47.8% (478/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -1.322547, rho = -0.893126 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 47.8% (478/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -1.865607, rho = -0.846267 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 47.8% (478/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -2.607416, rho = -0.778862 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 47.8% (478/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -3.593040, rho = -0.681905 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -4.842318, rho = -0.542436 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 78% (78/100) (classification) Accuracy = 70.3% (703/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -6.290705, rho = -0.341817 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 97% (97/100) (classification) Accuracy = 92.5% (925/1000) (classification) * optimization finished, #iter = 47 nu = 0.875078 obj = -7.871728, rho = -0.310227 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 99% (99/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 51 nu = 0.762719 obj = -9.684307, rho = -0.270986 nSV = 79, nBSV = 74 Total nSV = 79 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 58 nu = 0.643017 obj = -11.810216, rho = -0.229245 nSV = 67, nBSV = 62 Total nSV = 67 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 36 nu = 0.553219 obj = -14.495687, rho = -0.167749 nSV = 57, nBSV = 54 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 37 nu = 0.477866 obj = -17.710874, rho = -0.090688 nSV = 49, nBSV = 46 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 69 nu = 0.413668 obj = -21.302333, rho = -0.033494 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 60 nu = 0.342261 obj = -25.381009, rho = -0.008122 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 51 nu = 0.281240 obj = -30.264541, rho = -0.066123 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 53 nu = 0.234043 obj = -36.398844, rho = -0.052576 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 82 nu = 0.198148 obj = -43.417296, rho = -0.068024 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 76 nu = 0.167294 obj = -50.923861, rho = -0.004588 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 54 nu = 0.137843 obj = -58.984261, rho = 0.015181 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 85 nu = 0.114689 obj = -66.894080, rho = -0.113624 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -0.939650, rho = -0.926923 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.338804, rho = -0.894882 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.899246, rho = -0.848793 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.677021, rho = -0.782496 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.737062, rho = -0.687132 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -5.140318, rho = -0.549954 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 53% (53/100) (classification) Accuracy = 52.7% (527/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -6.907310, rho = -0.352632 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 84% (84/100) (classification) Accuracy = 86.8% (868/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -8.944913, rho = -0.196641 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 53 nu = 0.866915 obj = -11.129155, rho = -0.206191 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.759410 obj = -13.548087, rho = -0.135754 nSV = 79, nBSV = 74 Total nSV = 79 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 63 nu = 0.650605 obj = -16.232500, rho = -0.144882 nSV = 68, nBSV = 61 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 40 nu = 0.541723 obj = -19.323680, rho = -0.161045 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 36 nu = 0.445701 obj = -22.952218, rho = -0.181372 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 41 nu = 0.375377 obj = -27.152573, rho = -0.141366 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 52 nu = 0.307249 obj = -31.851661, rho = -0.134860 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 58 nu = 0.251124 obj = -37.151615, rho = -0.168811 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 72 nu = 0.202007 obj = -43.182194, rho = -0.217727 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 79 nu = 0.168285 obj = -50.073241, rho = -0.304019 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *..* optimization finished, #iter = 227 nu = 0.134066 obj = -57.495833, rho = -0.378467 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.106340 obj = -66.131483, rho = -0.410176 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -0.931279, rho = 0.851056 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.321483, rho = 0.785752 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.863407, rho = 0.691815 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.602865, rho = 0.556691 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.583622, rho = 0.362322 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 56% (56/100) (classification) Accuracy = 52.9% (529/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.822831, rho = 0.082732 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 82% (82/100) (classification) Accuracy = 83.2% (832/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -6.263971, rho = -0.234608 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 97% (97/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 50 nu = 0.852446 obj = -7.940830, rho = -0.185824 nSV = 88, nBSV = 83 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 40 nu = 0.764851 obj = -9.930557, rho = -0.161809 nSV = 78, nBSV = 76 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 44 nu = 0.662807 obj = -12.194664, rho = -0.138285 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 49 nu = 0.578805 obj = -14.747426, rho = 0.011272 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 31 nu = 0.502077 obj = -17.618718, rho = -0.033949 nSV = 52, nBSV = 49 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 94 nu = 0.415730 obj = -20.523705, rho = -0.053471 nSV = 45, nBSV = 37 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 48 nu = 0.337552 obj = -23.832760, rho = -0.111155 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 85 nu = 0.270751 obj = -27.371849, rho = -0.145996 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 57 nu = 0.217260 obj = -31.584753, rho = -0.186480 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 67 nu = 0.174624 obj = -36.119933, rho = -0.282057 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 226 nu = 0.140158 obj = -41.633956, rho = -0.300002 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 93 nu = 0.115807 obj = -47.139123, rho = -0.464232 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 75 nu = 0.091962 obj = -52.066404, rho = -0.493397 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -0.895030, rho = -0.927048 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 54.5% (545/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.271707, rho = -0.895062 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 54.5% (545/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -1.796701, rho = -0.849052 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 54.5% (545/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -2.517040, rho = -0.782870 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 54.5% (545/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -3.481125, rho = -0.687669 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 54.6% (546/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -4.718758, rho = -0.550727 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 70% (70/100) (classification) Accuracy = 73.6% (736/1000) (classification) * optimization finished, #iter = 46 nu = 0.918408 obj = -6.190440, rho = -0.357597 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 92% (92/100) (classification) Accuracy = 91.9% (919/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -7.889654, rho = -0.347996 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 93% (93/100) (classification) Accuracy = 94% (940/1000) (classification) * optimization finished, #iter = 47 nu = 0.746678 obj = -9.927984, rho = -0.286786 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 96% (96/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 39 nu = 0.662354 obj = -12.322103, rho = -0.249891 nSV = 68, nBSV = 66 Total nSV = 68 Accuracy = 97% (97/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 47 nu = 0.577085 obj = -15.074882, rho = -0.317195 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 50 nu = 0.490809 obj = -18.342535, rho = -0.285423 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.415335 obj = -22.279301, rho = -0.317181 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 70 nu = 0.346142 obj = -27.195769, rho = -0.264644 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 184 nu = 0.293234 obj = -33.401536, rho = -0.224771 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 67 nu = 0.258519 obj = -41.107129, rho = -0.150896 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 71 nu = 0.216984 obj = -50.167448, rho = -0.149716 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 56 nu = 0.191264 obj = -60.331367, rho = -0.132429 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 164 nu = 0.152059 obj = -72.287647, rho = -0.137905 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 64 nu = 0.125542 obj = -89.045618, rho = -0.124171 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -0.898241, rho = -0.936954 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48.7% (487/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.278351, rho = -0.909312 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48.7% (487/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.810449, rho = -0.869550 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48.7% (487/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -2.545487, rho = -0.812354 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48.7% (487/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -3.539987, rho = -0.730080 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48.7% (487/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -4.840551, rho = -0.611734 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 69% (69/100) (classification) Accuracy = 56.6% (566/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -6.442414, rho = -0.441499 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 85% (85/100) (classification) Accuracy = 86.8% (868/1000) (classification) * optimization finished, #iter = 50 nu = 0.866466 obj = -8.293646, rho = -0.353522 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 93% (93/100) (classification) Accuracy = 93.9% (939/1000) (classification) * optimization finished, #iter = 48 nu = 0.780000 obj = -10.501877, rho = -0.298746 nSV = 80, nBSV = 76 Total nSV = 80 Accuracy = 95% (95/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 49 nu = 0.682457 obj = -13.271112, rho = -0.279670 nSV = 71, nBSV = 65 Total nSV = 71 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 36 nu = 0.614504 obj = -16.717141, rho = -0.210518 nSV = 63, nBSV = 60 Total nSV = 63 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 39 nu = 0.541996 obj = -20.654385, rho = -0.214456 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.465115 obj = -25.352293, rho = -0.218795 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 38 nu = 0.399299 obj = -30.971879, rho = -0.178904 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 60 nu = 0.340995 obj = -37.716352, rho = -0.149842 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 41 nu = 0.286472 obj = -45.950769, rho = -0.136406 nSV = 31, nBSV = 27 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.242840 obj = -55.489516, rho = -0.158553 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 150 nu = 0.208668 obj = -66.922415, rho = -0.206020 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.174842 obj = -79.864720, rho = -0.214306 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 132 nu = 0.144281 obj = -94.291649, rho = -0.290308 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -0.949906, rho = -0.889152 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.347412, rho = -0.840551 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.898912, rho = -0.770641 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -2.650230, rho = -0.670078 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -3.644085, rho = -0.525424 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 64% (64/100) (classification) Accuracy = 63.5% (635/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -4.893932, rho = -0.317347 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 90% (90/100) (classification) Accuracy = 91.8% (918/1000) (classification) * optimization finished, #iter = 51 nu = 0.945547 obj = -6.352721, rho = -0.157966 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 96% (96/100) (classification) Accuracy = 94.4% (944/1000) (classification) * optimization finished, #iter = 54 nu = 0.844070 obj = -8.125674, rho = -0.113935 nSV = 87, nBSV = 83 Total nSV = 87 Accuracy = 96% (96/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 42 nu = 0.776280 obj = -10.322343, rho = -0.087346 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 96% (96/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 57 nu = 0.702533 obj = -12.788137, rho = -0.106665 nSV = 72, nBSV = 68 Total nSV = 72 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 56 nu = 0.598321 obj = -15.616525, rho = -0.139117 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 61 nu = 0.501206 obj = -19.139039, rho = -0.178610 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 97% (97/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 41 nu = 0.432466 obj = -23.494397, rho = -0.163491 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 69 nu = 0.365053 obj = -28.762664, rho = -0.100715 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 44 nu = 0.306842 obj = -35.655289, rho = -0.096325 nSV = 33, nBSV = 29 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 51 nu = 0.266746 obj = -44.126435, rho = -0.164451 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 44 nu = 0.225756 obj = -54.976886, rho = -0.177582 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 65 nu = 0.198029 obj = -69.004159, rho = -0.311603 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 58 nu = 0.168585 obj = -86.685847, rho = -0.370869 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 50 nu = 0.151659 obj = -109.104478, rho = -0.504531 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -0.951208, rho = -0.921244 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.350106, rho = -0.886713 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.904487, rho = -0.837043 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -2.661765, rho = -0.765594 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -3.667952, rho = -0.662819 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -4.943317, rho = -0.514982 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 75% (75/100) (classification) Accuracy = 71.5% (715/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -6.422006, rho = -0.302326 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 92% (92/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 51 nu = 0.885314 obj = -8.019684, rho = -0.259368 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 45 nu = 0.770935 obj = -9.901794, rho = -0.202797 nSV = 78, nBSV = 76 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 45 nu = 0.681147 obj = -12.024740, rho = -0.104511 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 74 nu = 0.573673 obj = -14.326092, rho = -0.044505 nSV = 61, nBSV = 54 Total nSV = 61 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 42 nu = 0.478995 obj = -17.015943, rho = 0.003255 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 85 nu = 0.391440 obj = -20.130985, rho = -0.012495 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 81 nu = 0.327387 obj = -23.726391, rho = 0.068637 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 47 nu = 0.267283 obj = -27.849652, rho = 0.126411 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 32 nu = 0.221954 obj = -32.350842, rho = 0.148990 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 99 nu = 0.181807 obj = -36.644294, rho = 0.243591 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 96 nu = 0.145025 obj = -41.074421, rho = 0.322483 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 173 nu = 0.111385 obj = -46.118560, rho = 0.333614 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 90 nu = 0.086141 obj = -51.505747, rho = 0.285186 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -0.915067, rho = -0.927143 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.300553, rho = -0.895198 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.838243, rho = -0.849248 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -2.576896, rho = -0.783151 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -3.567432, rho = -0.688074 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.843335, rho = -0.551310 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 59% (59/100) (classification) Accuracy = 59.7% (597/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.370491, rho = -0.354581 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 94% (94/100) (classification) Accuracy = 90.4% (904/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -8.013889, rho = -0.205307 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 44 nu = 0.790516 obj = -9.832694, rho = -0.125334 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 59 nu = 0.667313 obj = -11.881710, rho = -0.102403 nSV = 70, nBSV = 65 Total nSV = 70 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 45 nu = 0.560026 obj = -14.381572, rho = -0.068720 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 54 nu = 0.467272 obj = -17.435431, rho = -0.021522 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 33 nu = 0.404744 obj = -21.084614, rho = 0.055836 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 45 nu = 0.345238 obj = -25.065998, rho = 0.096722 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .*.* optimization finished, #iter = 223 nu = 0.279725 obj = -29.446472, rho = 0.110329 nSV = 33, nBSV = 23 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.228752 obj = -34.920170, rho = 0.047493 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 52 nu = 0.190497 obj = -41.142966, rho = -0.022384 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 62 nu = 0.157540 obj = -48.157074, rho = 0.092680 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.134283 obj = -55.492609, rho = 0.225306 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) ...*.* optimization finished, #iter = 453 nu = 0.101535 obj = -63.001325, rho = 0.204817 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -0.891284, rho = 0.863639 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.263957, rho = 0.803851 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.780666, rho = 0.717849 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -2.483862, rho = 0.594140 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -3.412475, rho = 0.416191 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 58% (58/100) (classification) Accuracy = 52.1% (521/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -4.576711, rho = 0.160221 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 83% (83/100) (classification) Accuracy = 78.2% (782/1000) (classification) * optimization finished, #iter = 49 nu = 0.883987 obj = -5.919886, rho = -0.032948 nSV = 91, nBSV = 87 Total nSV = 91 Accuracy = 95% (95/100) (classification) Accuracy = 92.8% (928/1000) (classification) * optimization finished, #iter = 41 nu = 0.800000 obj = -7.505742, rho = -0.042430 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 95% (95/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 45 nu = 0.718221 obj = -9.375938, rho = -0.011067 nSV = 73, nBSV = 70 Total nSV = 73 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 54 nu = 0.632576 obj = -11.576731, rho = -0.063332 nSV = 66, nBSV = 61 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 36 nu = 0.540106 obj = -14.160202, rho = -0.022071 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 31 nu = 0.476413 obj = -17.237314, rho = -0.090018 nSV = 48, nBSV = 45 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 38 nu = 0.394106 obj = -20.695464, rho = -0.140796 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 84 nu = 0.325118 obj = -24.972128, rho = -0.103011 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.269361 obj = -30.588568, rho = -0.075502 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 53 nu = 0.227210 obj = -37.759514, rho = -0.053697 nSV = 26, nBSV = 21 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 71 nu = 0.200368 obj = -46.229214, rho = 0.061876 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 99 nu = 0.170381 obj = -56.123840, rho = 0.198826 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 80 nu = 0.141633 obj = -68.532037, rho = 0.339913 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 67 nu = 0.120956 obj = -84.117529, rho = 0.381944 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -0.875439, rho = 0.909112 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -1.243785, rho = 0.869262 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -1.757070, rho = 0.811940 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -2.461138, rho = 0.729486 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -3.402999, rho = 0.610879 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.1% (501/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -4.611108, rho = 0.440269 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 69% (69/100) (classification) Accuracy = 64.2% (642/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -6.045346, rho = 0.194855 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 95% (95/100) (classification) Accuracy = 92.1% (921/1000) (classification) * optimization finished, #iter = 44 nu = 0.829081 obj = -7.656444, rho = 0.135370 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 99% (99/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 40 nu = 0.740000 obj = -9.500609, rho = 0.126590 nSV = 75, nBSV = 73 Total nSV = 75 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 63 nu = 0.652076 obj = -11.597637, rho = 0.104072 nSV = 67, nBSV = 61 Total nSV = 67 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 30 nu = 0.560000 obj = -13.933556, rho = 0.112217 nSV = 57, nBSV = 55 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 84 nu = 0.470991 obj = -16.391865, rho = 0.139120 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 41 nu = 0.380829 obj = -19.303232, rho = 0.140305 nSV = 40, nBSV = 37 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 58 nu = 0.313037 obj = -22.597159, rho = 0.130804 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 38 nu = 0.255013 obj = -26.635304, rho = 0.107360 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 34 nu = 0.211240 obj = -31.096473, rho = 0.146959 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.173751 obj = -35.630152, rho = 0.133895 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 76 nu = 0.138166 obj = -40.714373, rho = 0.176439 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *..* optimization finished, #iter = 244 nu = 0.108953 obj = -46.194601, rho = 0.218004 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 64 nu = 0.084621 obj = -53.140301, rho = 0.154773 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -0.872751, rho = -0.939286 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -1.238223, rho = -0.912666 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -1.745561, rho = -0.874374 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -2.437325, rho = -0.819293 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -3.353726, rho = -0.740062 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 57% (57/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -4.509156, rho = -0.626093 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 78% (78/100) (classification) Accuracy = 70.8% (708/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -5.849392, rho = -0.498473 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 94% (94/100) (classification) Accuracy = 88.5% (885/1000) (classification) * optimization finished, #iter = 43 nu = 0.796330 obj = -7.395333, rho = -0.424656 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 96% (96/100) (classification) Accuracy = 93.5% (935/1000) (classification) * optimization finished, #iter = 41 nu = 0.700000 obj = -9.266956, rho = -0.373476 nSV = 71, nBSV = 69 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 36 nu = 0.624029 obj = -11.466227, rho = -0.310073 nSV = 64, nBSV = 62 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 33 nu = 0.540000 obj = -14.002041, rho = -0.335569 nSV = 56, nBSV = 53 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 33 nu = 0.467658 obj = -16.873926, rho = -0.278265 nSV = 48, nBSV = 44 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.387882 obj = -20.046479, rho = -0.318341 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 56 nu = 0.324770 obj = -23.831563, rho = -0.273169 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 35 nu = 0.270994 obj = -28.139212, rho = -0.261471 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 74 nu = 0.221742 obj = -32.859242, rho = -0.324319 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 87 nu = 0.186792 obj = -37.992358, rho = -0.307263 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 54 nu = 0.143633 obj = -43.490172, rho = -0.341446 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 75 nu = 0.116764 obj = -49.982967, rho = -0.357124 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 157 nu = 0.095385 obj = -56.829682, rho = -0.285695 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -0.921385, rho = 0.934043 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 47.1% (471/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.313625, rho = 0.905124 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 47.1% (471/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.865291, rho = 0.863526 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 47.1% (471/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.632862, rho = 0.803689 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 47.1% (471/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.683234, rho = 0.717616 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 47.1% (471/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -5.082946, rho = 0.593805 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 47.4% (474/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -6.866279, rho = 0.415710 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 70% (70/100) (classification) Accuracy = 69.3% (693/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -8.955446, rho = 0.162089 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 97% (97/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 50 nu = 0.871348 obj = -11.223031, rho = 0.050345 nSV = 89, nBSV = 85 Total nSV = 89 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 42 nu = 0.743688 obj = -13.861120, rho = 0.114763 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 42 nu = 0.640000 obj = -17.095137, rho = 0.152792 nSV = 67, nBSV = 63 Total nSV = 67 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 55 nu = 0.555093 obj = -20.964263, rho = 0.148381 nSV = 58, nBSV = 51 Total nSV = 58 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 42 nu = 0.484651 obj = -25.575901, rho = 0.026967 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 88 nu = 0.398504 obj = -31.054282, rho = 0.074504 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 48 nu = 0.339987 obj = -38.038056, rho = 0.217724 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 80 nu = 0.292295 obj = -46.455012, rho = 0.127720 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) *..* optimization finished, #iter = 216 nu = 0.247301 obj = -55.868257, rho = 0.084745 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 59 nu = 0.205511 obj = -67.113967, rho = 0.107154 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.173907 obj = -81.361840, rho = 0.114424 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 215 nu = 0.143549 obj = -97.742445, rho = 0.145008 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.950012, rho = -0.899875 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.347632, rho = -0.855975 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.899368, rho = -0.792827 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.651173, rho = -0.701992 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.646036, rho = -0.571330 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 58% (58/100) (classification) Accuracy = 53.3% (533/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.897970, rho = -0.383380 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 88% (88/100) (classification) Accuracy = 84.6% (846/1000) (classification) * optimization finished, #iter = 48 nu = 0.950681 obj = -6.353097, rho = -0.226288 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 95% (95/100) (classification) Accuracy = 94.6% (946/1000) (classification) * optimization finished, #iter = 47 nu = 0.849791 obj = -8.098962, rho = -0.226946 nSV = 87, nBSV = 83 Total nSV = 87 Accuracy = 96% (96/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 43 nu = 0.773340 obj = -10.200858, rho = -0.109584 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 96% (96/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 38 nu = 0.680000 obj = -12.671577, rho = -0.064882 nSV = 69, nBSV = 67 Total nSV = 69 Accuracy = 96% (96/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 39 nu = 0.592459 obj = -15.529936, rho = -0.144155 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 36 nu = 0.512083 obj = -18.947273, rho = -0.101710 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 98 nu = 0.431262 obj = -22.903172, rho = -0.144148 nSV = 48, nBSV = 38 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 36 nu = 0.365972 obj = -27.774512, rho = -0.110572 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 68 nu = 0.311102 obj = -33.077392, rho = -0.082547 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 51 nu = 0.265941 obj = -38.795729, rho = -0.111710 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 66 nu = 0.210963 obj = -44.919629, rho = -0.182001 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.174943 obj = -52.242152, rho = -0.280049 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.139829 obj = -60.469920, rho = -0.236661 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 259 nu = 0.111684 obj = -69.624457, rho = -0.231737 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -0.894025, rho = 0.881869 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.269628, rho = 0.830075 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.792400, rho = 0.755571 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -2.508141, rho = 0.648401 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 50.2% (502/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -3.462712, rho = 0.494242 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -4.680657, rho = 0.272493 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 74% (74/100) (classification) Accuracy = 69.6% (696/1000) (classification) * optimization finished, #iter = 52 nu = 0.917776 obj = -6.111637, rho = -0.039577 nSV = 93, nBSV = 90 Total nSV = 93 Accuracy = 96% (96/100) (classification) Accuracy = 94.4% (944/1000) (classification) * optimization finished, #iter = 46 nu = 0.840000 obj = -7.743208, rho = -0.123843 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 98% (98/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 41 nu = 0.741349 obj = -9.627623, rho = -0.222567 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 41 nu = 0.650299 obj = -11.851282, rho = -0.171060 nSV = 68, nBSV = 64 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 51 nu = 0.561422 obj = -14.342692, rho = -0.220299 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 37 nu = 0.471958 obj = -17.365215, rho = -0.161053 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 41 nu = 0.402786 obj = -20.851091, rho = -0.162340 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.339526 obj = -24.839626, rho = -0.124389 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 78 nu = 0.279179 obj = -29.378438, rho = -0.058816 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 73 nu = 0.226134 obj = -34.701500, rho = 0.046535 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 32 nu = 0.191179 obj = -41.226868, rho = -0.009390 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.155844 obj = -48.028784, rho = -0.082284 nSV = 21, nBSV = 10 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.126040 obj = -56.254621, rho = -0.069299 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *...* optimization finished, #iter = 307 nu = 0.102953 obj = -65.738052, rho = -0.240979 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.950395, rho = 0.856208 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.8% (518/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.348424, rho = 0.793162 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.8% (518/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.901006, rho = 0.702474 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.8% (518/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.654562, rho = 0.572023 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.8% (518/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.653049, rho = 0.384377 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 58% (58/100) (classification) Accuracy = 55.7% (557/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.912481, rho = 0.114457 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 83% (83/100) (classification) Accuracy = 85.1% (851/1000) (classification) * optimization finished, #iter = 48 nu = 0.951583 obj = -6.391747, rho = -0.121054 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 94% (94/100) (classification) Accuracy = 94.4% (944/1000) (classification) * optimization finished, #iter = 47 nu = 0.850982 obj = -8.169018, rho = -0.189347 nSV = 88, nBSV = 84 Total nSV = 88 Accuracy = 95% (95/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 40 nu = 0.760000 obj = -10.418442, rho = -0.179543 nSV = 76, nBSV = 76 Total nSV = 76 Accuracy = 96% (96/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 46 nu = 0.688315 obj = -13.113553, rho = -0.129413 nSV = 71, nBSV = 66 Total nSV = 71 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 44 nu = 0.607036 obj = -16.391978, rho = -0.078887 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 42 nu = 0.529410 obj = -20.198589, rho = -0.191893 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 38 nu = 0.457981 obj = -24.762039, rho = -0.255682 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 77 nu = 0.390635 obj = -29.978924, rho = -0.142048 nSV = 42, nBSV = 34 Total nSV = 42 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 62 nu = 0.328621 obj = -36.603140, rho = -0.107857 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 153 nu = 0.276209 obj = -44.437179, rho = -0.126717 nSV = 32, nBSV = 23 Total nSV = 32 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 70 nu = 0.230052 obj = -54.363162, rho = -0.176058 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 79 nu = 0.194889 obj = -67.339997, rho = -0.258677 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.170039 obj = -83.540052, rho = -0.427223 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.140885 obj = -104.553179, rho = -0.388460 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -0.894996, rho = 0.900545 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 51.1% (511/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -1.271638, rho = 0.856939 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 51.1% (511/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -1.796558, rho = 0.794215 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 51.1% (511/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -2.516743, rho = 0.703988 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 51.1% (511/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -3.480511, rho = 0.574202 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 51.1% (511/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -4.717488, rho = 0.387510 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 70% (70/100) (classification) Accuracy = 62.5% (625/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -6.187781, rho = 0.119055 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 88% (88/100) (classification) Accuracy = 88.6% (886/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -7.842682, rho = 0.071106 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 95% (95/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 50 nu = 0.756533 obj = -9.661741, rho = 0.111522 nSV = 78, nBSV = 74 Total nSV = 78 Accuracy = 96% (96/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 41 nu = 0.656814 obj = -11.770656, rho = 0.082862 nSV = 68, nBSV = 63 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 43 nu = 0.560000 obj = -14.168873, rho = 0.063396 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 74 nu = 0.469615 obj = -16.857171, rho = 0.146649 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 0.379977 obj = -20.265066, rho = 0.124401 nSV = 42, nBSV = 34 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 39 nu = 0.323949 obj = -24.446249, rho = 0.068747 nSV = 35, nBSV = 31 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 69 nu = 0.274970 obj = -29.068054, rho = 0.078093 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.221728 obj = -34.530136, rho = 0.109740 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 49 nu = 0.189057 obj = -41.295237, rho = 0.133802 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 36 nu = 0.156419 obj = -48.947999, rho = 0.057245 nSV = 17, nBSV = 13 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 62 nu = 0.126441 obj = -58.108074, rho = 0.121181 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 179 nu = 0.100912 obj = -70.247041, rho = 0.129090 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.943674, rho = 0.821394 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 47.9% (479/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.334518, rho = 0.743085 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 47.9% (479/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.872233, rho = 0.630440 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 47.9% (479/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.595027, rho = 0.468407 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 47.9% (479/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.529862, rho = 0.235330 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 68% (68/100) (classification) Accuracy = 62.8% (628/1000) (classification) * optimization finished, #iter = 49 nu = 0.977982 obj = -4.657624, rho = -0.090482 nSV = 98, nBSV = 96 Total nSV = 98 Accuracy = 95% (95/100) (classification) Accuracy = 89.6% (896/1000) (classification) * optimization finished, #iter = 47 nu = 0.900479 obj = -5.986929, rho = -0.141042 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 97% (97/100) (classification) Accuracy = 93.5% (935/1000) (classification) * optimization finished, #iter = 45 nu = 0.823283 obj = -7.517308, rho = -0.163721 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 49 nu = 0.731411 obj = -9.230628, rho = -0.162137 nSV = 76, nBSV = 72 Total nSV = 76 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 41 nu = 0.634250 obj = -11.161766, rho = -0.167709 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 55 nu = 0.531027 obj = -13.341152, rho = -0.205233 nSV = 54, nBSV = 51 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.449671 obj = -15.767525, rho = -0.257215 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 47 nu = 0.373784 obj = -18.447042, rho = -0.321348 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 46 nu = 0.299544 obj = -21.384573, rho = -0.335329 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 71 nu = 0.237926 obj = -25.102851, rho = -0.369117 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 25 nu = 0.195355 obj = -29.750504, rho = -0.320902 nSV = 22, nBSV = 17 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 70 nu = 0.162177 obj = -34.725535, rho = -0.235136 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 40 nu = 0.133155 obj = -40.612878, rho = -0.295993 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 38 nu = 0.109836 obj = -46.811414, rho = -0.419770 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 63 nu = 0.093606 obj = -51.971065, rho = -0.359067 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -0.914841, rho = -0.939630 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.300086, rho = -0.913161 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.837277, rho = -0.875087 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.574898, rho = -0.820319 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.563298, rho = -0.741537 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -4.834781, rho = -0.628215 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 77% (77/100) (classification) Accuracy = 69.3% (693/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -6.352793, rho = -0.465205 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 92% (92/100) (classification) Accuracy = 89.3% (893/1000) (classification) * optimization finished, #iter = 56 nu = 0.870163 obj = -8.038626, rho = -0.378909 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 95% (95/100) (classification) Accuracy = 95.1% (951/1000) (classification) * optimization finished, #iter = 41 nu = 0.760000 obj = -10.070072, rho = -0.361581 nSV = 77, nBSV = 75 Total nSV = 77 Accuracy = 97% (97/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 39 nu = 0.687270 obj = -12.441415, rho = -0.273824 nSV = 70, nBSV = 67 Total nSV = 70 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 43 nu = 0.590508 obj = -15.080344, rho = -0.226608 nSV = 61, nBSV = 58 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 43 nu = 0.494515 obj = -18.174051, rho = -0.223096 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 51 nu = 0.416032 obj = -21.904454, rho = -0.162680 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.342889 obj = -26.366473, rho = -0.176998 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 68 nu = 0.284781 obj = -32.044295, rho = -0.231647 nSV = 34, nBSV = 25 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 50 nu = 0.248320 obj = -38.806606, rho = -0.111883 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*..* optimization finished, #iter = 308 nu = 0.209756 obj = -46.329423, rho = -0.137759 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.167727 obj = -55.865883, rho = -0.122417 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 96 nu = 0.144356 obj = -67.705196, rho = -0.168593 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 142 nu = 0.123913 obj = -81.086968, rho = -0.285851 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -0.897375, rho = 0.904998 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 46.8% (468/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -1.276559, rho = 0.863344 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 46.8% (468/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -1.806741, rho = 0.803427 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 46.8% (468/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -2.537814, rho = 0.717240 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 46.8% (468/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -3.524109, rho = 0.593264 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 46.8% (468/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -4.807697, rho = 0.414930 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 65% (65/100) (classification) Accuracy = 57.1% (571/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -6.374434, rho = 0.158406 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 88% (88/100) (classification) Accuracy = 91% (910/1000) (classification) * optimization finished, #iter = 44 nu = 0.868048 obj = -8.195965, rho = 0.043433 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 96% (96/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 46 nu = 0.794246 obj = -10.236652, rho = 0.022763 nSV = 81, nBSV = 77 Total nSV = 81 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 57 nu = 0.690048 obj = -12.637731, rho = -0.044781 nSV = 72, nBSV = 65 Total nSV = 72 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 63 nu = 0.594184 obj = -15.358254, rho = -0.140379 nSV = 64, nBSV = 58 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 63 nu = 0.504072 obj = -18.549196, rho = -0.202264 nSV = 54, nBSV = 47 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 43 nu = 0.430204 obj = -22.424638, rho = -0.195911 nSV = 45, nBSV = 41 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 50 nu = 0.363584 obj = -26.814081, rho = -0.290177 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.301445 obj = -31.579759, rho = -0.305139 nSV = 35, nBSV = 26 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 77 nu = 0.245488 obj = -37.287010, rho = -0.325240 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *....* optimization finished, #iter = 440 nu = 0.200370 obj = -44.184972, rho = -0.324058 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *.....* optimization finished, #iter = 567 nu = 0.163939 obj = -52.530975, rho = -0.294961 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.139438 obj = -62.215292, rho = -0.218436 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.115721 obj = -73.346189, rho = -0.221435 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -0.931391, rho = -0.924534 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 53.2% (532/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.321715, rho = -0.891446 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 53.2% (532/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.863887, rho = -0.843850 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 53.2% (532/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.603857, rho = -0.775386 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 53.2% (532/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.585676, rho = -0.676905 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 55% (55/100) (classification) Accuracy = 54.2% (542/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.827080, rho = -0.535243 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 79% (79/100) (classification) Accuracy = 79.6% (796/1000) (classification) * optimization finished, #iter = 47 nu = 0.936055 obj = -6.281207, rho = -0.378907 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 97% (97/100) (classification) Accuracy = 94.3% (943/1000) (classification) * optimization finished, #iter = 48 nu = 0.866924 obj = -7.941247, rho = -0.299430 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 47 nu = 0.762074 obj = -9.872505, rho = -0.254485 nSV = 79, nBSV = 74 Total nSV = 79 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 40 nu = 0.663889 obj = -12.141829, rho = -0.232820 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 60 nu = 0.578622 obj = -14.703317, rho = -0.196517 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 39 nu = 0.490004 obj = -17.643032, rho = -0.157547 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 28 nu = 0.404608 obj = -21.056167, rho = -0.086054 nSV = 43, nBSV = 40 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 61 nu = 0.339748 obj = -24.951245, rho = -0.109120 nSV = 38, nBSV = 30 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 72 nu = 0.274091 obj = -29.764789, rho = -0.111837 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 37 nu = 0.233690 obj = -35.260287, rho = -0.294211 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 62 nu = 0.189861 obj = -41.528411, rho = -0.435577 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.158018 obj = -49.175318, rho = -0.617867 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 45 nu = 0.137577 obj = -56.267353, rho = -0.875382 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 46 nu = 0.108732 obj = -62.665296, rho = -0.842385 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -0.879197, rho = -0.930365 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48.1% (481/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -1.251561, rho = -0.899833 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48.1% (481/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -1.773166, rho = -0.856113 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 55% (55/100) (classification) Accuracy = 48.1% (481/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -2.494443, rho = -0.793025 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 55% (55/100) (classification) Accuracy = 48.1% (481/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -3.471913, rho = -0.702277 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 55% (55/100) (classification) Accuracy = 48.1% (481/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -4.753701, rho = -0.571741 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 59% (59/100) (classification) Accuracy = 53.2% (532/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -6.340389, rho = -0.383971 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 89% (89/100) (classification) Accuracy = 82.9% (829/1000) (classification) * optimization finished, #iter = 47 nu = 0.860627 obj = -8.147060, rho = -0.193815 nSV = 89, nBSV = 85 Total nSV = 89 Accuracy = 96% (96/100) (classification) Accuracy = 94.6% (946/1000) (classification) * optimization finished, #iter = 42 nu = 0.775749 obj = -10.226711, rho = -0.137544 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 44 nu = 0.685228 obj = -12.686123, rho = -0.109545 nSV = 72, nBSV = 66 Total nSV = 72 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 47 nu = 0.603906 obj = -15.494885, rho = -0.128129 nSV = 63, nBSV = 57 Total nSV = 63 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 39 nu = 0.504781 obj = -18.824475, rho = -0.102572 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 46 nu = 0.434486 obj = -22.705775, rho = -0.112964 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 29 nu = 0.364479 obj = -27.136986, rho = -0.179682 nSV = 38, nBSV = 34 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 59 nu = 0.302193 obj = -32.194413, rho = -0.164089 nSV = 35, nBSV = 26 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 69 nu = 0.251579 obj = -38.410989, rho = -0.181599 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.203538 obj = -45.665812, rho = -0.216950 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 133 nu = 0.169590 obj = -54.874366, rho = -0.170658 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..*.* optimization finished, #iter = 300 nu = 0.139339 obj = -66.361837, rho = -0.149011 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 155 nu = 0.116542 obj = -80.998523, rho = -0.155450 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 41 nu = 0.800000 obj = -0.785306, rho = -0.954467 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 41 nu = 0.800000 obj = -1.120355, rho = -0.934503 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 41 nu = 0.800000 obj = -1.592399, rho = -0.905786 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 41 nu = 0.800000 obj = -2.250909, rho = -0.864478 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 41 nu = 0.800000 obj = -3.155722, rho = -0.805058 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 41 nu = 0.800000 obj = -4.369475, rho = -0.719586 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 41 nu = 0.800000 obj = -5.933782, rho = -0.596639 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 70% (70/100) (classification) Accuracy = 60.5% (605/1000) (classification) * optimization finished, #iter = 41 nu = 0.800000 obj = -7.808168, rho = -0.419785 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 92% (92/100) (classification) Accuracy = 90.1% (901/1000) (classification) * optimization finished, #iter = 44 nu = 0.749233 obj = -9.874018, rho = -0.328407 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 49 nu = 0.654367 obj = -12.259146, rho = -0.299591 nSV = 69, nBSV = 63 Total nSV = 69 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 43 nu = 0.577595 obj = -15.086368, rho = -0.211219 nSV = 59, nBSV = 56 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 31 nu = 0.500000 obj = -18.367145, rho = -0.100346 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 46 nu = 0.420000 obj = -22.065843, rho = -0.030333 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.348220 obj = -26.455869, rho = -0.020549 nSV = 38, nBSV = 33 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 62 nu = 0.297994 obj = -31.428986, rho = -0.081416 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.244438 obj = -37.184104, rho = -0.015353 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 74 nu = 0.200576 obj = -44.151646, rho = -0.027083 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 63 nu = 0.163326 obj = -52.672335, rho = -0.054495 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.138719 obj = -62.604899, rho = -0.169659 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.111849 obj = -74.139699, rho = -0.169029 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -0.932015, rho = -0.913818 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 47.8% (478/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.323006, rho = -0.876031 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 47.8% (478/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.866558, rho = -0.821677 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 47.8% (478/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.609385, rho = -0.743491 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 47.8% (478/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.597114, rho = -0.631025 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 54% (54/100) (classification) Accuracy = 48.7% (487/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.850747, rho = -0.469248 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 72% (72/100) (classification) Accuracy = 74.8% (748/1000) (classification) * optimization finished, #iter = 52 nu = 0.948935 obj = -6.309642, rho = -0.294060 nSV = 96, nBSV = 92 Total nSV = 96 Accuracy = 94% (94/100) (classification) Accuracy = 93.9% (939/1000) (classification) * optimization finished, #iter = 47 nu = 0.865038 obj = -7.982618, rho = -0.195362 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.760000 obj = -9.989364, rho = -0.182070 nSV = 77, nBSV = 75 Total nSV = 77 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.674455 obj = -12.280249, rho = -0.150179 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 40 nu = 0.582406 obj = -14.981528, rho = -0.095944 nSV = 60, nBSV = 57 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 47 nu = 0.499791 obj = -17.981883, rho = -0.152260 nSV = 53, nBSV = 46 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 43 nu = 0.418278 obj = -21.455521, rho = -0.133291 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 31 nu = 0.341315 obj = -25.607578, rho = -0.138560 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 91 nu = 0.284484 obj = -30.368327, rho = -0.188057 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 62 nu = 0.232873 obj = -36.391846, rho = -0.176739 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 154 nu = 0.194314 obj = -43.435732, rho = -0.074733 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 89 nu = 0.159297 obj = -52.680378, rho = -0.141314 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 59 nu = 0.135916 obj = -64.317383, rho = -0.124079 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.116213 obj = -77.232712, rho = 0.004930 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -0.895175, rho = 0.882517 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 48.7% (487/1000) (classification) * optimization finished, #iter = 50 nu = 0.920000 obj = -1.272013, rho = 0.831443 nSV = 94, nBSV = 90 Total nSV = 94 Accuracy = 54% (54/100) (classification) Accuracy = 48.7% (487/1000) (classification) * optimization finished, #iter = 52 nu = 0.920000 obj = -1.797339, rho = 0.757405 nSV = 95, nBSV = 90 Total nSV = 95 Accuracy = 54% (54/100) (classification) Accuracy = 48.7% (487/1000) (classification) * optimization finished, #iter = 52 nu = 0.920000 obj = -2.518360, rho = 0.651039 nSV = 95, nBSV = 90 Total nSV = 95 Accuracy = 54% (54/100) (classification) Accuracy = 48.7% (487/1000) (classification) * optimization finished, #iter = 52 nu = 0.920000 obj = -3.483856, rho = 0.498037 nSV = 95, nBSV = 90 Total nSV = 95 Accuracy = 55% (55/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -4.724415, rho = 0.277446 nSV = 95, nBSV = 90 Total nSV = 95 Accuracy = 69% (69/100) (classification) Accuracy = 66.2% (662/1000) (classification) * optimization finished, #iter = 50 nu = 0.900000 obj = -6.207484, rho = 0.018609 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 87% (87/100) (classification) Accuracy = 87.9% (879/1000) (classification) * optimization finished, #iter = 46 nu = 0.844967 obj = -7.903943, rho = -0.164015 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 98% (98/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 38 nu = 0.747890 obj = -9.912920, rho = -0.158118 nSV = 76, nBSV = 74 Total nSV = 76 Accuracy = 99% (99/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 34 nu = 0.680000 obj = -12.236030, rho = -0.179739 nSV = 68, nBSV = 68 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 47 nu = 0.587227 obj = -14.694347, rho = -0.192411 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 51 nu = 0.495403 obj = -17.464300, rho = -0.258278 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 52 nu = 0.415578 obj = -20.462111, rho = -0.327950 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.332113 obj = -23.612154, rho = -0.323673 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 96 nu = 0.267084 obj = -27.471684, rho = -0.284530 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.214072 obj = -32.325703, rho = -0.293406 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 144 nu = 0.177338 obj = -37.688265, rho = -0.313019 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 163 nu = 0.143337 obj = -44.232133, rho = -0.351677 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.119591 obj = -51.860620, rho = -0.354523 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 137 nu = 0.094242 obj = -60.258309, rho = -0.355086 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 43 nu = 0.820000 obj = -0.802820, rho = 0.930785 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 43 nu = 0.820000 obj = -1.143982, rho = 0.900438 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 43 nu = 0.820000 obj = -1.623141, rho = 0.856785 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 43 nu = 0.820000 obj = -2.288419, rho = 0.793992 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 43 nu = 0.820000 obj = -3.195792, rho = 0.703668 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 48.6% (486/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -4.398383, rho = 0.573741 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -5.915914, rho = 0.386847 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 80% (80/100) (classification) Accuracy = 65.2% (652/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -7.659456, rho = 0.118011 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 94% (94/100) (classification) Accuracy = 91.3% (913/1000) (classification) * optimization finished, #iter = 44 nu = 0.734314 obj = -9.559427, rho = 0.049249 nSV = 75, nBSV = 71 Total nSV = 75 Accuracy = 97% (97/100) (classification) Accuracy = 94.3% (943/1000) (classification) * optimization finished, #iter = 36 nu = 0.641507 obj = -11.794092, rho = 0.024607 nSV = 66, nBSV = 63 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 31 nu = 0.557373 obj = -14.423734, rho = 0.094961 nSV = 56, nBSV = 54 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 43 nu = 0.464211 obj = -17.519298, rho = 0.156933 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 29 nu = 0.400000 obj = -21.337557, rho = 0.158090 nSV = 41, nBSV = 37 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 39 nu = 0.335463 obj = -25.746190, rho = 0.099478 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 47 nu = 0.281308 obj = -31.270457, rho = 0.064706 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 34 nu = 0.238544 obj = -38.139295, rho = 0.054261 nSV = 26, nBSV = 21 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 23 nu = 0.203883 obj = -46.259684, rho = 0.188353 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 73 nu = 0.179277 obj = -55.179629, rho = 0.214197 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 75 nu = 0.144118 obj = -65.027095, rho = 0.218110 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 92 nu = 0.117501 obj = -77.075122, rho = 0.278765 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -0.934313, rho = 0.903771 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -1.327763, rho = 0.861616 nSV = 97, nBSV = 94 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -1.876400, rho = 0.800941 nSV = 97, nBSV = 94 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -2.629750, rho = 0.713664 nSV = 97, nBSV = 94 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 51 nu = 0.960000 obj = -3.639253, rho = 0.588418 nSV = 97, nBSV = 94 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 51 nu = 0.960000 obj = -4.937938, rho = 0.407961 nSV = 97, nBSV = 94 Total nSV = 97 Accuracy = 68% (68/100) (classification) Accuracy = 60.7% (607/1000) (classification) * optimization finished, #iter = 53 nu = 0.960000 obj = -6.488556, rho = 0.148699 nSV = 97, nBSV = 94 Total nSV = 97 Accuracy = 97% (97/100) (classification) Accuracy = 91.1% (911/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -8.150225, rho = 0.034974 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 51 nu = 0.788684 obj = -10.030668, rho = 0.109738 nSV = 82, nBSV = 76 Total nSV = 82 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 53 nu = 0.677190 obj = -12.266840, rho = 0.051999 nSV = 70, nBSV = 65 Total nSV = 70 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 38 nu = 0.572546 obj = -14.979911, rho = 0.060858 nSV = 59, nBSV = 56 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 42 nu = 0.494798 obj = -18.188009, rho = -0.057738 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 38 nu = 0.422521 obj = -21.760736, rho = -0.084121 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 48 nu = 0.357041 obj = -25.700294, rho = -0.050534 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 39 nu = 0.289433 obj = -30.202431, rho = -0.036224 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 47 nu = 0.242342 obj = -35.148917, rho = -0.008763 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) . WARNING: using -h 0 may be faster * optimization finished, #iter = 126 nu = 0.198681 obj = -40.007321, rho = 0.030292 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.153730 obj = -45.391468, rho = 0.084769 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 87 nu = 0.125207 obj = -51.719619, rho = 0.126942 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*...* optimization finished, #iter = 486 nu = 0.098976 obj = -57.307315, rho = 0.083150 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -0.913564, rho = 0.879086 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.297444, rho = 0.826071 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.831810, rho = 0.749811 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.563585, rho = 0.640116 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -3.539891, rho = 0.482325 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 54% (54/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.786349, rho = 0.255351 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 75% (75/100) (classification) Accuracy = 71.7% (717/1000) (classification) * optimization finished, #iter = 47 nu = 0.921333 obj = -6.256503, rho = -0.014141 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 95% (95/100) (classification) Accuracy = 93.1% (931/1000) (classification) * optimization finished, #iter = 45 nu = 0.855294 obj = -7.939360, rho = -0.060198 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 97% (97/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 56 nu = 0.759821 obj = -9.896493, rho = -0.003719 nSV = 78, nBSV = 73 Total nSV = 78 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 59 nu = 0.666684 obj = -12.190184, rho = -0.060138 nSV = 70, nBSV = 64 Total nSV = 70 Accuracy = 96% (96/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 35 nu = 0.575396 obj = -14.891945, rho = 0.028575 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 59 nu = 0.493130 obj = -17.937680, rho = -0.003824 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 52 nu = 0.405517 obj = -21.615174, rho = 0.026743 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 32 nu = 0.337665 obj = -26.338861, rho = 0.013798 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 58 nu = 0.290714 obj = -32.146160, rho = -0.034308 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 58 nu = 0.241246 obj = -39.151557, rho = 0.014568 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 69 nu = 0.205796 obj = -47.733596, rho = 0.023435 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 72 nu = 0.171496 obj = -58.830966, rho = 0.036031 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.148970 obj = -72.650772, rho = 0.101432 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.133454 obj = -88.319118, rho = 0.218309 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 47 nu = 0.860000 obj = -0.839813, rho = 0.920675 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 47 nu = 0.860000 obj = -1.195297, rho = 0.885895 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 47 nu = 0.860000 obj = -1.693030, rho = 0.835866 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -2.380830, rho = 0.763901 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -3.311917, rho = 0.660383 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 57% (57/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -4.530656, rho = 0.512573 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 59% (59/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -6.034241, rho = 0.298860 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 83% (83/100) (classification) Accuracy = 79.3% (793/1000) (classification) * optimization finished, #iter = 44 nu = 0.840000 obj = -7.694034, rho = 0.076206 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 40 nu = 0.760000 obj = -9.515863, rho = 0.018484 nSV = 76, nBSV = 76 Total nSV = 76 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 41 nu = 0.654846 obj = -11.454484, rho = -0.072796 nSV = 69, nBSV = 63 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 45 nu = 0.555212 obj = -13.694244, rho = -0.057486 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 47 nu = 0.452560 obj = -16.251203, rho = -0.056916 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 54 nu = 0.377140 obj = -19.266311, rho = -0.017238 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 42 nu = 0.310595 obj = -22.915722, rho = 0.037744 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 41 nu = 0.262557 obj = -26.796362, rho = 0.173052 nSV = 30, nBSV = 21 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 77 nu = 0.210480 obj = -31.249904, rho = 0.177554 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 71 nu = 0.175888 obj = -36.309466, rho = 0.183995 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 53 nu = 0.138189 obj = -41.601263, rho = 0.186361 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 64 nu = 0.112966 obj = -47.881052, rho = 0.194340 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 94 nu = 0.090020 obj = -54.390017, rho = 0.145561 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -0.957063, rho = -0.919027 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.362220, rho = -0.883524 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.929553, rho = -0.832456 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -2.713629, rho = -0.758996 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -3.775267, rho = -0.653328 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -5.165367, rho = -0.501329 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 53% (53/100) (classification) Accuracy = 56.5% (565/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -6.881458, rho = -0.282687 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 87% (87/100) (classification) Accuracy = 90.6% (906/1000) (classification) * optimization finished, #iter = 48 nu = 0.950955 obj = -8.784873, rho = -0.065048 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 44 nu = 0.860335 obj = -10.803205, rho = 0.014855 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 50 nu = 0.731688 obj = -13.116420, rho = 0.058590 nSV = 75, nBSV = 69 Total nSV = 75 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.606083 obj = -15.990563, rho = 0.015724 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 55 nu = 0.523752 obj = -19.424193, rho = -0.013669 nSV = 56, nBSV = 49 Total nSV = 56 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 66 nu = 0.438669 obj = -23.525221, rho = 0.015785 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 62 nu = 0.369315 obj = -28.462702, rho = 0.057169 nSV = 39, nBSV = 35 Total nSV = 39 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 40 nu = 0.308007 obj = -34.750088, rho = 0.071119 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 72 nu = 0.262978 obj = -42.339993, rho = 0.143927 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.223028 obj = -51.717494, rho = 0.141410 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 86 nu = 0.184299 obj = -63.837873, rho = 0.120446 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 71 nu = 0.164439 obj = -79.099536, rho = 0.150872 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 75 nu = 0.138380 obj = -96.961391, rho = 0.275552 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 39 nu = 0.760000 obj = -0.746297, rho = -0.966602 nSV = 76, nBSV = 76 Total nSV = 76 Accuracy = 62% (62/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 39 nu = 0.760000 obj = -1.064869, rho = -0.951958 nSV = 76, nBSV = 76 Total nSV = 76 Accuracy = 62% (62/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 39 nu = 0.760000 obj = -1.513879, rho = -0.930894 nSV = 76, nBSV = 76 Total nSV = 76 Accuracy = 62% (62/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 39 nu = 0.760000 obj = -2.140639, rho = -0.900595 nSV = 76, nBSV = 76 Total nSV = 76 Accuracy = 62% (62/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 39 nu = 0.760000 obj = -3.002644, rho = -0.857011 nSV = 76, nBSV = 76 Total nSV = 76 Accuracy = 62% (62/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 39 nu = 0.760000 obj = -4.160745, rho = -0.794317 nSV = 76, nBSV = 76 Total nSV = 76 Accuracy = 62% (62/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 39 nu = 0.760000 obj = -5.657253, rho = -0.704136 nSV = 76, nBSV = 76 Total nSV = 76 Accuracy = 65% (65/100) (classification) Accuracy = 55.4% (554/1000) (classification) * optimization finished, #iter = 39 nu = 0.760000 obj = -7.459475, rho = -0.574414 nSV = 76, nBSV = 76 Total nSV = 76 Accuracy = 91% (91/100) (classification) Accuracy = 84.4% (844/1000) (classification) * optimization finished, #iter = 42 nu = 0.740000 obj = -9.337683, rho = -0.419809 nSV = 75, nBSV = 73 Total nSV = 75 Accuracy = 99% (99/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 52 nu = 0.640000 obj = -11.310975, rho = -0.373427 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 73 nu = 0.540267 obj = -13.445852, rho = -0.313142 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 43 nu = 0.447519 obj = -15.911362, rho = -0.299664 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 41 nu = 0.367104 obj = -18.814940, rho = -0.362873 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 58 nu = 0.305774 obj = -22.177617, rho = -0.432537 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 90 nu = 0.245926 obj = -26.239805, rho = -0.347285 nSV = 30, nBSV = 21 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 165 nu = 0.204836 obj = -30.958127, rho = -0.434237 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.166156 obj = -36.745818, rho = -0.494067 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 78 nu = 0.135053 obj = -44.157524, rho = -0.543029 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 75 nu = 0.115693 obj = -52.714116, rho = -0.452207 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.093031 obj = -63.364972, rho = -0.466019 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -0.918262, rho = -0.928223 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.7% (487/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.307165, rho = -0.896753 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.7% (487/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.851924, rho = -0.851484 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.7% (487/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -2.605205, rho = -0.786367 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.7% (487/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -3.626008, rho = -0.692700 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.7% (487/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.964536, rho = -0.557964 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 55% (55/100) (classification) Accuracy = 54.6% (546/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -6.621276, rho = -0.363441 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 86% (86/100) (classification) Accuracy = 86.4% (864/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -8.501204, rho = -0.176524 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 46 nu = 0.817589 obj = -10.609512, rho = -0.073991 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 42 nu = 0.710235 obj = -13.086585, rho = -0.057648 nSV = 73, nBSV = 69 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 53 nu = 0.617142 obj = -15.995445, rho = 0.055634 nSV = 64, nBSV = 58 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 36 nu = 0.528485 obj = -19.431482, rho = 0.077957 nSV = 54, nBSV = 51 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 51 nu = 0.441062 obj = -23.381459, rho = 0.024181 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 90 nu = 0.372857 obj = -27.915422, rho = 0.057756 nSV = 42, nBSV = 33 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.304293 obj = -33.610408, rho = 0.064674 nSV = 35, nBSV = 26 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 77 nu = 0.251974 obj = -41.013260, rho = 0.056691 nSV = 33, nBSV = 23 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 62 nu = 0.213966 obj = -50.550651, rho = 0.042634 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.184590 obj = -62.044378, rho = 0.032485 nSV = 25, nBSV = 15 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 99 nu = 0.157793 obj = -75.934005, rho = 0.062932 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 75 nu = 0.133375 obj = -93.398710, rho = 0.041665 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -0.895571, rho = -0.945816 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.272828, rho = -0.922059 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.799020, rho = -0.887885 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -2.521838, rho = -0.838729 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -3.491053, rho = -0.768019 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -4.739300, rho = -0.666307 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 67% (67/100) (classification) Accuracy = 63.7% (637/1000) (classification) * optimization finished, #iter = 47 nu = 0.913130 obj = -6.233836, rho = -0.532660 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 88% (88/100) (classification) Accuracy = 87.3% (873/1000) (classification) * optimization finished, #iter = 53 nu = 0.858704 obj = -7.986645, rho = -0.413197 nSV = 87, nBSV = 82 Total nSV = 87 Accuracy = 93% (93/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 39 nu = 0.757641 obj = -10.061585, rho = -0.363203 nSV = 76, nBSV = 74 Total nSV = 76 Accuracy = 96% (96/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 47 nu = 0.666228 obj = -12.562790, rho = -0.366259 nSV = 68, nBSV = 64 Total nSV = 68 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 35 nu = 0.590331 obj = -15.589429, rho = -0.373641 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 58 nu = 0.506537 obj = -19.088446, rho = -0.365260 nSV = 55, nBSV = 47 Total nSV = 55 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 54 nu = 0.434575 obj = -23.264840, rho = -0.277156 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 84 nu = 0.363009 obj = -28.305202, rho = -0.278957 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 51 nu = 0.305822 obj = -34.588221, rho = -0.287160 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 90 nu = 0.264022 obj = -42.327269, rho = -0.305364 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.222504 obj = -51.573658, rho = -0.303927 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 58 nu = 0.196005 obj = -62.410174, rho = -0.155785 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.160344 obj = -74.310220, rho = -0.218186 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 94 nu = 0.138555 obj = -87.132958, rho = -0.293901 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 43 nu = 0.820000 obj = -0.806053, rho = -0.965295 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 43 nu = 0.820000 obj = -1.150672, rho = -0.950079 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 43 nu = 0.820000 obj = -1.636983, rho = -0.928191 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 43 nu = 0.820000 obj = -2.317060, rho = -0.896707 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -3.255057, rho = -0.851703 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -4.521011, rho = -0.786683 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 59% (59/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -6.169648, rho = -0.693154 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 62% (62/100) (classification) Accuracy = 53.7% (537/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -8.184468, rho = -0.558337 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 82% (82/100) (classification) Accuracy = 82% (820/1000) (classification) * optimization finished, #iter = 42 nu = 0.780302 obj = -10.400046, rho = -0.420834 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 95% (95/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 51 nu = 0.699434 obj = -12.890978, rho = -0.330870 nSV = 72, nBSV = 67 Total nSV = 72 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 32 nu = 0.600000 obj = -15.881535, rho = -0.316930 nSV = 61, nBSV = 59 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 36 nu = 0.523739 obj = -19.315931, rho = -0.296329 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 46 nu = 0.444416 obj = -23.280571, rho = -0.274626 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 57 nu = 0.377937 obj = -27.811845, rho = -0.233248 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 50 nu = 0.310916 obj = -32.899420, rho = -0.245264 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 77 nu = 0.252598 obj = -39.147661, rho = -0.264396 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 63 nu = 0.211248 obj = -46.573031, rho = -0.317070 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 65 nu = 0.172202 obj = -55.786475, rho = -0.290710 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 96 nu = 0.146582 obj = -66.557781, rho = -0.251074 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 92 nu = 0.120916 obj = -79.032967, rho = -0.151815 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -0.859228, rho = -0.948427 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -1.222855, rho = -0.925814 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -1.731908, rho = -0.893288 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -2.435173, rho = -0.846499 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -3.386817, rho = -0.779197 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -4.631629, rho = -0.682386 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 63% (63/100) (classification) Accuracy = 58.9% (589/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -6.165488, rho = -0.543128 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 86% (86/100) (classification) Accuracy = 84% (840/1000) (classification) * optimization finished, #iter = 49 nu = 0.839681 obj = -7.908850, rho = -0.409993 nSV = 86, nBSV = 81 Total nSV = 86 Accuracy = 97% (97/100) (classification) Accuracy = 94.6% (946/1000) (classification) * optimization finished, #iter = 41 nu = 0.754869 obj = -9.926238, rho = -0.446799 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 99% (99/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 51 nu = 0.680000 obj = -12.255459, rho = -0.397570 nSV = 72, nBSV = 65 Total nSV = 72 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 43 nu = 0.582717 obj = -14.763414, rho = -0.364474 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 65 nu = 0.494311 obj = -17.489691, rho = -0.358965 nSV = 53, nBSV = 45 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 41 nu = 0.413010 obj = -20.654010, rho = -0.335376 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 39 nu = 0.335715 obj = -24.130846, rho = -0.367332 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 40 nu = 0.284550 obj = -27.630869, rho = -0.441177 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 155 nu = 0.222976 obj = -31.107754, rho = -0.444693 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 130 nu = 0.173156 obj = -35.106892, rho = -0.482259 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 139 nu = 0.138885 obj = -39.596377, rho = -0.531214 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 92 nu = 0.107882 obj = -44.540135, rho = -0.546055 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *....* optimization finished, #iter = 406 nu = 0.084151 obj = -49.737678, rho = -0.622644 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.972626, rho = 0.027614 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 93% (93/100) (classification) Accuracy = 93.2% (932/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.381809, rho = 0.039722 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 93% (93/100) (classification) Accuracy = 93.2% (932/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.951939, rho = 0.057138 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 93% (93/100) (classification) Accuracy = 93.2% (932/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.733851, rho = 0.082190 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 93% (93/100) (classification) Accuracy = 93.2% (932/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.779566, rho = 0.118227 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 93% (93/100) (classification) Accuracy = 93.2% (932/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -5.120258, rho = 0.170063 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 93% (93/100) (classification) Accuracy = 93.2% (932/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -6.737702, rho = 0.121850 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 94% (94/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -8.574893, rho = 0.039904 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 96% (96/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 45 nu = 0.834032 obj = -10.624973, rho = 0.088448 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 96% (96/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 39 nu = 0.725644 obj = -12.907700, rho = 0.044518 nSV = 74, nBSV = 70 Total nSV = 74 Accuracy = 96% (96/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 48 nu = 0.608655 obj = -15.615156, rho = 0.011588 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 96% (96/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 50 nu = 0.511683 obj = -18.857447, rho = 0.027782 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 96% (96/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 49 nu = 0.429615 obj = -22.684114, rho = -0.070453 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 191 nu = 0.362118 obj = -27.201922, rho = -0.010594 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 40 nu = 0.300480 obj = -32.763514, rho = -0.036821 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 93 nu = 0.247586 obj = -39.715973, rho = -0.076550 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 54 nu = 0.202845 obj = -48.960476, rho = -0.075473 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 36 nu = 0.184554 obj = -60.435844, rho = -0.135658 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.158426 obj = -72.421779, rho = -0.127590 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.130132 obj = -86.230406, rho = -0.055138 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -0.859872, rho = -0.934151 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -1.224188, rho = -0.905279 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -1.734667, rho = -0.863749 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -2.440882, rho = -0.804010 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -3.398631, rho = -0.718597 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -4.656072, rho = -0.595216 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 57% (57/100) (classification) Accuracy = 51.6% (516/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -6.216064, rho = -0.417739 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 82% (82/100) (classification) Accuracy = 82.7% (827/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -7.971936, rho = -0.244526 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 94% (94/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 43 nu = 0.764404 obj = -9.859323, rho = -0.126272 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 96% (96/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 42 nu = 0.677222 obj = -12.066761, rho = -0.110916 nSV = 69, nBSV = 64 Total nSV = 69 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 52 nu = 0.564448 obj = -14.649415, rho = -0.098889 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 97% (97/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 47 nu = 0.480000 obj = -17.810467, rho = -0.056480 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 48 nu = 0.400880 obj = -21.584879, rho = -0.075405 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 93 nu = 0.342421 obj = -26.085116, rho = -0.027009 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 49 nu = 0.286482 obj = -31.707405, rho = -0.011775 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 44 nu = 0.239554 obj = -38.623386, rho = -0.043926 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 45 nu = 0.202427 obj = -47.536116, rho = -0.132990 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 58 nu = 0.172102 obj = -58.803834, rho = -0.121848 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 80 nu = 0.146882 obj = -73.106265, rho = -0.094078 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.128470 obj = -91.292136, rho = 0.019569 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -0.804583, rho = 0.941988 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -1.147629, rho = 0.916553 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -1.630687, rho = 0.879966 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -2.304033, rho = 0.827336 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -3.228100, rho = 0.751632 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -4.465232, rho = 0.642735 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 49.4% (494/1000) (classification) * optimization finished, #iter = 43 nu = 0.820000 obj = -6.054233, rho = 0.486093 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 64% (64/100) (classification) Accuracy = 59.8% (598/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -7.945658, rho = 0.260770 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 90% (90/100) (classification) Accuracy = 90.2% (902/1000) (classification) * optimization finished, #iter = 39 nu = 0.780000 obj = -9.953788, rho = 0.072516 nSV = 78, nBSV = 78 Total nSV = 78 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 42 nu = 0.692363 obj = -12.013726, rho = -0.006077 nSV = 71, nBSV = 68 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.569783 obj = -14.309820, rho = -0.018324 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 48 nu = 0.469573 obj = -17.130710, rho = -0.047978 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 51 nu = 0.392006 obj = -20.592785, rho = -0.069877 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 46 nu = 0.335912 obj = -24.746150, rho = -0.119713 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 35 nu = 0.278217 obj = -29.171800, rho = -0.217101 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 54 nu = 0.229477 obj = -34.192979, rho = -0.307768 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 73 nu = 0.184435 obj = -40.029893, rho = -0.320040 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.147528 obj = -47.590217, rho = -0.372671 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 60 nu = 0.122708 obj = -57.495347, rho = -0.300410 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.105427 obj = -68.219875, rho = -0.293540 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -0.881974, rho = 0.909301 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 50 nu = 0.900000 obj = -1.257314, rho = 0.868213 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -1.785064, rho = 0.810432 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -2.519062, rho = 0.727315 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -3.522852, rho = 0.607757 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 49.8% (498/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -4.859101, rho = 0.435778 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 58% (58/100) (classification) Accuracy = 52.3% (523/1000) (classification) * optimization finished, #iter = 49 nu = 0.900000 obj = -6.558476, rho = 0.188395 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 85% (85/100) (classification) Accuracy = 78.3% (783/1000) (classification) * optimization finished, #iter = 46 nu = 0.876142 obj = -8.554577, rho = -0.088574 nSV = 90, nBSV = 86 Total nSV = 90 Accuracy = 94% (94/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 40 nu = 0.800000 obj = -10.940429, rho = -0.143170 nSV = 80, nBSV = 80 Total nSV = 80 Accuracy = 94% (94/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 45 nu = 0.723779 obj = -13.740211, rho = -0.143754 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 94% (94/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 47 nu = 0.640000 obj = -16.953223, rho = -0.189033 nSV = 66, nBSV = 61 Total nSV = 66 Accuracy = 94% (94/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 50 nu = 0.546379 obj = -20.761703, rho = -0.137150 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 94% (94/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 34 nu = 0.474577 obj = -25.270318, rho = -0.011628 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 94% (94/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 81 nu = 0.398310 obj = -30.607824, rho = -0.051264 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 95% (95/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 56 nu = 0.335786 obj = -37.122093, rho = -0.070833 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 96% (96/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 152 nu = 0.282631 obj = -45.048933, rho = -0.097932 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 96% (96/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 172 nu = 0.239458 obj = -54.724152, rho = -0.086771 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 137 nu = 0.198737 obj = -66.648422, rho = -0.139992 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 81 nu = 0.168970 obj = -81.812843, rho = -0.152849 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 99 nu = 0.147522 obj = -99.931256, rho = -0.133620 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -0.932523, rho = 0.842109 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -1.324059, rho = 0.772881 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -1.868736, rho = 0.673301 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -2.613890, rho = 0.530059 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 49.5% (495/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -3.606437, rho = 0.324014 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 54% (54/100) (classification) Accuracy = 51.9% (519/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -4.870037, rho = 0.027628 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 79% (79/100) (classification) Accuracy = 80.6% (806/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.368000, rho = -0.283373 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 91% (91/100) (classification) Accuracy = 93.9% (939/1000) (classification) * optimization finished, #iter = 50 nu = 0.855372 obj = -8.134304, rho = -0.300055 nSV = 88, nBSV = 83 Total nSV = 88 Accuracy = 95% (95/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 52 nu = 0.764052 obj = -10.295730, rho = -0.236427 nSV = 79, nBSV = 75 Total nSV = 79 Accuracy = 96% (96/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 41 nu = 0.669956 obj = -12.977387, rho = -0.234334 nSV = 68, nBSV = 65 Total nSV = 68 Accuracy = 96% (96/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 45 nu = 0.600000 obj = -16.279758, rho = -0.252067 nSV = 61, nBSV = 58 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 37 nu = 0.518063 obj = -20.287773, rho = -0.164468 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.455517 obj = -25.175377, rho = -0.095642 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 52 nu = 0.383746 obj = -31.196618, rho = -0.162137 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 54 nu = 0.337464 obj = -38.545456, rho = -0.083777 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 46 nu = 0.283855 obj = -47.919901, rho = -0.090587 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 48 nu = 0.249703 obj = -59.860123, rho = 0.008498 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 74 nu = 0.211552 obj = -74.996209, rho = 0.015610 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.197015 obj = -92.656686, rho = -0.093597 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 80 nu = 0.168259 obj = -110.879875, rho = -0.234737 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -0.856527, rho = 0.898627 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -1.217268, rho = 0.854181 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -1.720348, rho = 0.790246 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -2.411253, rho = 0.698279 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -3.337325, rho = 0.565990 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -4.529222, rho = 0.375698 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 65% (65/100) (classification) Accuracy = 59% (590/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -5.953595, rho = 0.101166 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 92% (92/100) (classification) Accuracy = 87% (870/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -7.521566, rho = -0.001905 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 96% (96/100) (classification) Accuracy = 94.6% (946/1000) (classification) * optimization finished, #iter = 42 nu = 0.732543 obj = -9.304650, rho = -0.090955 nSV = 75, nBSV = 71 Total nSV = 75 Accuracy = 97% (97/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 41 nu = 0.644354 obj = -11.295478, rho = -0.136908 nSV = 66, nBSV = 63 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 65 nu = 0.546231 obj = -13.382486, rho = -0.089051 nSV = 59, nBSV = 52 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 39 nu = 0.454767 obj = -15.702550, rho = -0.130923 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 63 nu = 0.368860 obj = -18.240236, rho = -0.068130 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 44 nu = 0.293723 obj = -21.303911, rho = -0.057677 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 46 nu = 0.237152 obj = -25.168031, rho = -0.041823 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 57 nu = 0.192916 obj = -30.075230, rho = -0.060347 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 52 nu = 0.162168 obj = -36.010308, rho = 0.066759 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 36 nu = 0.134995 obj = -42.662740, rho = 0.013592 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 71 nu = 0.114885 obj = -50.161514, rho = -0.066593 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 93 nu = 0.100582 obj = -55.741850, rho = -0.200883 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.961128, rho = -0.032677 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 99% (99/100) (classification) Accuracy = 94% (940/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.358019, rho = -0.047004 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 99% (99/100) (classification) Accuracy = 94% (940/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.902715, rho = -0.067613 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 99% (99/100) (classification) Accuracy = 94% (940/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.631998, rho = -0.097257 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 99% (99/100) (classification) Accuracy = 94% (940/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.568818, rho = -0.139900 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 99% (99/100) (classification) Accuracy = 94% (940/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.689288, rho = -0.177325 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 99% (99/100) (classification) Accuracy = 94.2% (942/1000) (classification) * optimization finished, #iter = 46 nu = 0.915374 obj = -5.979944, rho = -0.222844 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 99% (99/100) (classification) Accuracy = 94.6% (946/1000) (classification) * optimization finished, #iter = 45 nu = 0.821066 obj = -7.501421, rho = -0.253948 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 46 nu = 0.713052 obj = -9.313047, rho = -0.235409 nSV = 74, nBSV = 69 Total nSV = 74 Accuracy = 99% (99/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 47 nu = 0.634007 obj = -11.421163, rho = -0.264939 nSV = 65, nBSV = 60 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 33 nu = 0.540000 obj = -13.840841, rho = -0.281301 nSV = 55, nBSV = 53 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 29 nu = 0.456754 obj = -16.783478, rho = -0.286573 nSV = 46, nBSV = 43 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 32 nu = 0.389065 obj = -20.118849, rho = -0.338134 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 83 nu = 0.325225 obj = -23.704425, rho = -0.304424 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 45 nu = 0.273573 obj = -27.753541, rho = -0.314453 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.215996 obj = -32.163546, rho = -0.293567 nSV = 27, nBSV = 17 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 76 nu = 0.174303 obj = -37.772875, rho = -0.255344 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 86 nu = 0.141649 obj = -44.830157, rho = -0.343204 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..* optimization finished, #iter = 232 nu = 0.124613 obj = -52.140774, rho = -0.620387 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 161 nu = 0.096474 obj = -59.321324, rho = -0.639069 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -0.899107, rho = 0.913211 nSV = 94, nBSV = 91 Total nSV = 94 Accuracy = 54% (54/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -1.280144, rho = 0.875158 nSV = 94, nBSV = 91 Total nSV = 94 Accuracy = 54% (54/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -1.814159, rho = 0.820421 nSV = 94, nBSV = 91 Total nSV = 94 Accuracy = 54% (54/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 50 nu = 0.920000 obj = -2.553165, rho = 0.740882 nSV = 94, nBSV = 91 Total nSV = 94 Accuracy = 54% (54/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 51 nu = 0.920000 obj = -3.555874, rho = 0.627865 nSV = 94, nBSV = 91 Total nSV = 94 Accuracy = 54% (54/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 51 nu = 0.920000 obj = -4.873423, rho = 0.464702 nSV = 94, nBSV = 91 Total nSV = 94 Accuracy = 58% (58/100) (classification) Accuracy = 54.4% (544/1000) (classification) * optimization finished, #iter = 50 nu = 0.920000 obj = -6.510430, rho = 0.230001 nSV = 94, nBSV = 91 Total nSV = 94 Accuracy = 82% (82/100) (classification) Accuracy = 83.2% (832/1000) (classification) * optimization finished, #iter = 45 nu = 0.882976 obj = -8.363915, rho = 0.068008 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 96% (96/100) (classification) Accuracy = 94.6% (946/1000) (classification) * optimization finished, #iter = 41 nu = 0.808361 obj = -10.479098, rho = -0.028329 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 44 nu = 0.707219 obj = -12.914852, rho = -0.058604 nSV = 72, nBSV = 69 Total nSV = 72 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 41 nu = 0.603017 obj = -15.755437, rho = -0.002352 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 56 nu = 0.520000 obj = -19.146592, rho = -0.085749 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 40 nu = 0.443160 obj = -23.030593, rho = -0.171586 nSV = 47, nBSV = 43 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 56 nu = 0.373161 obj = -27.334520, rho = -0.161548 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 80 nu = 0.302343 obj = -32.372950, rho = -0.130067 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 47 nu = 0.248893 obj = -38.718511, rho = -0.065581 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 179 nu = 0.210771 obj = -45.915281, rho = -0.184420 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.169065 obj = -54.788971, rho = -0.176910 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 134 nu = 0.137569 obj = -66.680480, rho = -0.227342 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 59 nu = 0.119996 obj = -81.895169, rho = -0.376450 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -0.873443, rho = -0.927146 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -1.239655, rho = -0.895203 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -1.748526, rho = -0.849254 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -2.443458, rho = -0.783160 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.3% (503/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -3.366418, rho = -0.688086 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 55% (55/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -4.535416, rho = -0.551327 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 74% (74/100) (classification) Accuracy = 65.5% (655/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -5.888729, rho = -0.354607 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 93% (93/100) (classification) Accuracy = 89.8% (898/1000) (classification) * optimization finished, #iter = 43 nu = 0.819767 obj = -7.349218, rho = -0.262103 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 97% (97/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 41 nu = 0.727083 obj = -8.986199, rho = -0.212355 nSV = 75, nBSV = 71 Total nSV = 75 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 40 nu = 0.615378 obj = -10.829918, rho = -0.162896 nSV = 63, nBSV = 60 Total nSV = 63 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 51 nu = 0.515572 obj = -12.975714, rho = -0.153338 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 31 nu = 0.435828 obj = -15.505777, rho = -0.159831 nSV = 45, nBSV = 41 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 62 nu = 0.357453 obj = -18.388370, rho = -0.159213 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 32 nu = 0.289545 obj = -21.950254, rho = -0.190758 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 62 nu = 0.251040 obj = -26.095863, rho = -0.096170 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.207579 obj = -30.319771, rho = -0.021651 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *....* optimization finished, #iter = 484 nu = 0.169199 obj = -35.114580, rho = 0.022213 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 72 nu = 0.137416 obj = -40.292544, rho = -0.025858 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 82 nu = 0.110183 obj = -45.697092, rho = -0.080425 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 73 nu = 0.089709 obj = -49.273264, rho = -0.246434 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -0.911249, rho = 0.876885 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -1.292653, rho = 0.822905 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -1.821897, rho = 0.745258 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -2.543074, rho = 0.633567 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -3.497451, rho = 0.472904 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 54% (54/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -4.698535, rho = 0.241799 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 77% (77/100) (classification) Accuracy = 73% (730/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -6.077151, rho = -0.013192 nSV = 94, nBSV = 91 Total nSV = 94 Accuracy = 95% (95/100) (classification) Accuracy = 93.4% (934/1000) (classification) * optimization finished, #iter = 46 nu = 0.838930 obj = -7.645261, rho = -0.078153 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 96% (96/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 43 nu = 0.749967 obj = -9.420568, rho = -0.091747 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 67 nu = 0.631014 obj = -11.472599, rho = -0.099001 nSV = 67, nBSV = 61 Total nSV = 67 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 43 nu = 0.539036 obj = -13.991578, rho = -0.026371 nSV = 57, nBSV = 51 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 45 nu = 0.458428 obj = -16.958107, rho = -0.026085 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 50 nu = 0.381805 obj = -20.551347, rho = -0.078828 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 47 nu = 0.333665 obj = -24.699389, rho = 0.006383 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 64 nu = 0.271358 obj = -29.442997, rho = 0.038222 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 92 nu = 0.226016 obj = -35.523756, rho = 0.094522 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 94 nu = 0.185774 obj = -42.981862, rho = 0.135193 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.155707 obj = -52.314434, rho = 0.076133 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 75 nu = 0.128438 obj = -64.893558, rho = 0.080661 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.111785 obj = -81.978891, rho = 0.190103 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -0.859548, rho = -0.938626 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -1.223519, rho = -0.911717 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -1.733281, rho = -0.873009 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -2.438015, rho = -0.817330 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -3.392699, rho = -0.737239 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -4.643799, rho = -0.622032 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 58% (58/100) (classification) Accuracy = 53.1% (531/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -6.190668, rho = -0.456311 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 84% (84/100) (classification) Accuracy = 79.4% (794/1000) (classification) * optimization finished, #iter = 51 nu = 0.845998 obj = -7.930147, rho = -0.292705 nSV = 87, nBSV = 83 Total nSV = 87 Accuracy = 95% (95/100) (classification) Accuracy = 94.7% (947/1000) (classification) * optimization finished, #iter = 45 nu = 0.769677 obj = -9.846158, rho = -0.178027 nSV = 79, nBSV = 75 Total nSV = 79 Accuracy = 96% (96/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 54 nu = 0.666442 obj = -12.000917, rho = -0.204896 nSV = 69, nBSV = 64 Total nSV = 69 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.560255 obj = -14.599245, rho = -0.183908 nSV = 59, nBSV = 52 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 57 nu = 0.482335 obj = -17.714669, rho = -0.158613 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 50 nu = 0.404364 obj = -21.420558, rho = -0.149607 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 52 nu = 0.344719 obj = -25.794220, rho = -0.187999 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 198 nu = 0.286397 obj = -30.681353, rho = -0.218713 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 68 nu = 0.238432 obj = -36.500941, rho = -0.254563 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.200912 obj = -43.138210, rho = -0.362069 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 60 nu = 0.171443 obj = -49.590109, rho = -0.385462 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.130096 obj = -56.599473, rho = -0.377247 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 92 nu = 0.103631 obj = -65.975796, rho = -0.376144 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -0.934479, rho = 0.889876 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -1.328106, rho = 0.841593 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -1.877110, rho = 0.772139 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -2.631219, rho = 0.672233 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 50 nu = 0.960000 obj = -3.642291, rho = 0.528524 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 52% (52/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -4.944225, rho = 0.321806 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 64% (64/100) (classification) Accuracy = 66.6% (666/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -6.501565, rho = 0.024451 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 94% (94/100) (classification) Accuracy = 92.8% (928/1000) (classification) * optimization finished, #iter = 47 nu = 0.892765 obj = -8.246363, rho = -0.108754 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 96% (96/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 44 nu = 0.805111 obj = -10.228951, rho = -0.185123 nSV = 82, nBSV = 79 Total nSV = 82 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 41 nu = 0.693741 obj = -12.429732, rho = -0.214400 nSV = 73, nBSV = 67 Total nSV = 73 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 52 nu = 0.586686 obj = -15.053268, rho = -0.235325 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 60 nu = 0.495502 obj = -18.153145, rho = -0.210747 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 50 nu = 0.423330 obj = -21.663305, rho = -0.213208 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 54 nu = 0.347994 obj = -25.793649, rho = -0.218766 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.287074 obj = -30.796773, rho = -0.269826 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 89 nu = 0.236766 obj = -36.773826, rho = -0.232081 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 85 nu = 0.193546 obj = -44.275253, rho = -0.212890 nSV = 25, nBSV = 15 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 91 nu = 0.162532 obj = -53.876281, rho = -0.239244 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 79 nu = 0.137661 obj = -65.549679, rho = -0.245986 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 196 nu = 0.119957 obj = -78.884738, rho = -0.079635 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.961751, rho = -0.058229 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 90% (90/100) (classification) Accuracy = 84.3% (843/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.359307, rho = -0.083759 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 90% (90/100) (classification) Accuracy = 84.3% (843/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.905380, rho = -0.120484 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 90% (90/100) (classification) Accuracy = 84.3% (843/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.637514, rho = -0.173310 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 90% (90/100) (classification) Accuracy = 84.3% (843/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.580230, rho = -0.249297 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 90% (90/100) (classification) Accuracy = 84.3% (843/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.720093, rho = -0.350736 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 91% (91/100) (classification) Accuracy = 84.9% (849/1000) (classification) * optimization finished, #iter = 46 nu = 0.909433 obj = -6.060882, rho = -0.349680 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 91% (91/100) (classification) Accuracy = 89.2% (892/1000) (classification) * optimization finished, #iter = 45 nu = 0.822350 obj = -7.694501, rho = -0.354918 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 94% (94/100) (classification) Accuracy = 93.7% (937/1000) (classification) * optimization finished, #iter = 69 nu = 0.718341 obj = -9.712647, rho = -0.341691 nSV = 76, nBSV = 70 Total nSV = 76 Accuracy = 96% (96/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 36 nu = 0.639235 obj = -12.242852, rho = -0.269208 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 53 nu = 0.561370 obj = -15.342363, rho = -0.292559 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 44 nu = 0.497490 obj = -18.893284, rho = -0.289612 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.426458 obj = -23.110152, rho = -0.311084 nSV = 46, nBSV = 38 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 82 nu = 0.357838 obj = -28.275776, rho = -0.277418 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 46 nu = 0.312571 obj = -34.601836, rho = -0.277845 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 42 nu = 0.263313 obj = -42.283047, rho = -0.326494 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 71 nu = 0.228725 obj = -50.918191, rho = -0.449561 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.189606 obj = -60.909335, rho = -0.325540 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*.* optimization finished, #iter = 258 nu = 0.158835 obj = -72.524602, rho = -0.358081 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..*..* optimization finished, #iter = 489 nu = 0.131180 obj = -86.027777, rho = -0.400312 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -0.944366, rho = 0.800460 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 46.2% (462/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.335949, rho = 0.712971 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 46.2% (462/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.875194, rho = 0.587123 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 46.2% (462/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -2.601153, rho = 0.406098 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 52% (52/100) (classification) Accuracy = 46.4% (464/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.542539, rho = 0.145701 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 76% (76/100) (classification) Accuracy = 70.7% (707/1000) (classification) * optimization finished, #iter = 51 nu = 0.964061 obj = -4.686321, rho = -0.144988 nSV = 98, nBSV = 95 Total nSV = 98 Accuracy = 97% (97/100) (classification) Accuracy = 92.1% (921/1000) (classification) * optimization finished, #iter = 45 nu = 0.897091 obj = -6.059529, rho = -0.221313 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 99% (99/100) (classification) Accuracy = 94.5% (945/1000) (classification) * optimization finished, #iter = 45 nu = 0.841264 obj = -7.686437, rho = -0.261879 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 40 nu = 0.739515 obj = -9.463612, rho = -0.284002 nSV = 74, nBSV = 72 Total nSV = 74 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 50 nu = 0.647743 obj = -11.493704, rho = -0.239079 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 43 nu = 0.539550 obj = -13.847493, rho = -0.267713 nSV = 57, nBSV = 51 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 37 nu = 0.459645 obj = -16.708551, rho = -0.209437 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 38 nu = 0.395287 obj = -19.782578, rho = -0.229564 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 97 nu = 0.320281 obj = -23.132718, rho = -0.309002 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 65 nu = 0.261997 obj = -26.927402, rho = -0.351409 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.211636 obj = -31.250334, rho = -0.304017 nSV = 27, nBSV = 17 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 45 nu = 0.174279 obj = -36.285143, rho = -0.297019 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 88 nu = 0.139343 obj = -41.791506, rho = -0.243191 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 172 nu = 0.111409 obj = -47.971833, rho = -0.188178 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.087741 obj = -55.276416, rho = -0.155208 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -0.931316, rho = -0.905065 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.321562, rho = -0.863441 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -1.863569, rho = -0.803566 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.603199, rho = -0.717440 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.584315, rho = -0.593551 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 53% (53/100) (classification) Accuracy = 51.7% (517/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.824265, rho = -0.415343 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 80% (80/100) (classification) Accuracy = 77.2% (772/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.259741, rho = -0.203748 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 96% (96/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 44 nu = 0.857565 obj = -7.898595, rho = -0.070757 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 45 nu = 0.765466 obj = -9.778484, rho = -0.069197 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.683731 obj = -11.778277, rho = 0.002411 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.568651 obj = -13.899126, rho = -0.001169 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 44 nu = 0.465029 obj = -16.301176, rho = 0.048140 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 75 nu = 0.379963 obj = -19.190950, rho = 0.101662 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 42 nu = 0.315141 obj = -22.611823, rho = 0.089232 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 42 nu = 0.250457 obj = -26.642629, rho = 0.077423 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 52 nu = 0.210319 obj = -31.350957, rho = 0.075914 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 76 nu = 0.170017 obj = -36.741564, rho = 0.135790 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 91 nu = 0.135036 obj = -43.715313, rho = 0.130770 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 62 nu = 0.118177 obj = -52.296138, rho = -0.050952 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 155 nu = 0.093846 obj = -61.216590, rho = -0.073741 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -0.840536, rho = 0.922191 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -1.196793, rho = 0.888075 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -1.696126, rho = 0.839002 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -2.387236, rho = 0.768412 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -3.325172, rho = 0.666872 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -4.558080, rho = 0.520812 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 57% (57/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -6.090987, rho = 0.310712 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 86% (86/100) (classification) Accuracy = 76% (760/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -7.798223, rho = 0.008494 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 99% (99/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 42 nu = 0.760985 obj = -9.492924, rho = -0.052777 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 48 nu = 0.645117 obj = -11.473303, rho = 0.004663 nSV = 68, nBSV = 63 Total nSV = 68 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 40 nu = 0.548750 obj = -13.709431, rho = -0.035401 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 49 nu = 0.454119 obj = -16.369626, rho = -0.060252 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 31 nu = 0.382495 obj = -19.461675, rho = -0.031032 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 39 nu = 0.319125 obj = -23.007151, rho = 0.006947 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 73 nu = 0.262415 obj = -26.909811, rho = 0.112940 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.213356 obj = -31.441687, rho = 0.082490 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 73 nu = 0.172902 obj = -36.289264, rho = 0.004166 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 73 nu = 0.143317 obj = -41.605560, rho = 0.068795 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 90 nu = 0.117923 obj = -46.482169, rho = 0.150650 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 156 nu = 0.090561 obj = -50.244638, rho = 0.275396 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -0.802181, rho = 0.918216 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -1.142658, rho = 0.882358 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -1.620403, rho = 0.830778 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -2.282753, rho = 0.756583 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -3.184069, rho = 0.649856 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 59% (59/100) (classification) Accuracy = 50% (500/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -4.374125, rho = 0.496336 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 60% (60/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -5.865722, rho = 0.275505 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 82% (82/100) (classification) Accuracy = 78.1% (781/1000) (classification) * optimization finished, #iter = 41 nu = 0.809173 obj = -7.557695, rho = -0.004807 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 49 nu = 0.734917 obj = -9.403720, rho = -0.040647 nSV = 76, nBSV = 72 Total nSV = 76 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 36 nu = 0.653974 obj = -11.405075, rho = -0.053264 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 36 nu = 0.540681 obj = -13.652647, rho = -0.055653 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 47 nu = 0.453063 obj = -16.260543, rho = -0.046189 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 58 nu = 0.374459 obj = -19.296018, rho = -0.046292 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 38 nu = 0.305044 obj = -23.120962, rho = -0.044140 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 38 nu = 0.257231 obj = -27.650833, rho = -0.117182 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 51 nu = 0.220253 obj = -32.767967, rho = -0.257183 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.177961 obj = -38.362578, rho = -0.326125 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 168 nu = 0.145672 obj = -45.331301, rho = -0.353490 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 85 nu = 0.120532 obj = -52.628405, rho = -0.418082 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 69 nu = 0.098962 obj = -60.665447, rho = -0.397548 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -0.899747, rho = -0.938978 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.281468, rho = -0.912223 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.816898, rho = -0.873738 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -2.558831, rho = -0.818378 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -3.567596, rho = -0.738746 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 50.5% (505/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -4.897679, rho = -0.624199 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 58% (58/100) (classification) Accuracy = 53.4% (534/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -6.560618, rho = -0.459429 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 80% (80/100) (classification) Accuracy = 81.7% (817/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -8.496990, rho = -0.313164 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 93% (93/100) (classification) Accuracy = 94.1% (941/1000) (classification) * optimization finished, #iter = 44 nu = 0.814061 obj = -10.710156, rho = -0.307758 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 95% (95/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 45 nu = 0.705664 obj = -13.374314, rho = -0.286832 nSV = 72, nBSV = 68 Total nSV = 72 Accuracy = 96% (96/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 43 nu = 0.617541 obj = -16.667455, rho = -0.209578 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 96% (96/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 41 nu = 0.542818 obj = -20.724664, rho = -0.175906 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 60 nu = 0.463057 obj = -25.516485, rho = -0.063394 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 59 nu = 0.399306 obj = -31.392568, rho = -0.123289 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.338499 obj = -38.567351, rho = -0.086894 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 96 nu = 0.289813 obj = -47.666698, rho = -0.095149 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 96% (96/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.242163 obj = -59.268940, rho = -0.071547 nSV = 28, nBSV = 18 Total nSV = 28 Accuracy = 96% (96/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 177 nu = 0.213522 obj = -74.697050, rho = 0.054542 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 96% (96/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 182 nu = 0.183837 obj = -94.193540, rho = 0.113962 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 299 nu = 0.159522 obj = -119.525591, rho = 0.096278 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 96% (96/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.966897, rho = -0.036152 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.369956, rho = -0.052003 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.927414, rho = -0.074804 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.683105, rho = -0.107602 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.674565, rho = -0.154780 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -4.902997, rho = -0.222643 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 97% (97/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -6.321063, rho = -0.190897 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 99% (99/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 49 nu = 0.869512 obj = -7.954040, rho = -0.189336 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 43 nu = 0.767188 obj = -9.822039, rho = -0.183967 nSV = 79, nBSV = 75 Total nSV = 79 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 41 nu = 0.656625 obj = -12.021310, rho = -0.150896 nSV = 68, nBSV = 63 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 35 nu = 0.560000 obj = -14.777028, rho = -0.119739 nSV = 57, nBSV = 55 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 55 nu = 0.474710 obj = -18.069406, rho = -0.116772 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 34 nu = 0.410718 obj = -22.031886, rho = -0.137244 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 25 nu = 0.356346 obj = -26.709143, rho = -0.225390 nSV = 37, nBSV = 34 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 33 nu = 0.303657 obj = -31.809131, rho = -0.290732 nSV = 32, nBSV = 28 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.248904 obj = -37.193176, rho = -0.321384 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 147 nu = 0.200784 obj = -43.608113, rho = -0.374183 nSV = 25, nBSV = 15 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.163529 obj = -51.872759, rho = -0.430611 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 77 nu = 0.134733 obj = -61.476689, rho = -0.562740 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 152 nu = 0.113153 obj = -73.077632, rho = -0.743820 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 41 nu = 0.760000 obj = -0.743250, rho = 0.937003 nSV = 76, nBSV = 76 Total nSV = 76 Accuracy = 62% (62/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 42 nu = 0.760000 obj = -1.058564, rho = 0.908906 nSV = 77, nBSV = 75 Total nSV = 77 Accuracy = 62% (62/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 43 nu = 0.760000 obj = -1.500834, rho = 0.869100 nSV = 77, nBSV = 75 Total nSV = 77 Accuracy = 62% (62/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 43 nu = 0.760000 obj = -2.113647, rho = 0.811707 nSV = 77, nBSV = 75 Total nSV = 77 Accuracy = 62% (62/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 43 nu = 0.760000 obj = -2.946793, rho = 0.729150 nSV = 77, nBSV = 75 Total nSV = 77 Accuracy = 62% (62/100) (classification) Accuracy = 52% (520/1000) (classification) * optimization finished, #iter = 43 nu = 0.760000 obj = -4.045181, rho = 0.609986 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 62% (62/100) (classification) Accuracy = 52.1% (521/1000) (classification) * optimization finished, #iter = 43 nu = 0.760000 obj = -5.418135, rho = 0.438984 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 80% (80/100) (classification) Accuracy = 67.5% (675/1000) (classification) * optimization finished, #iter = 40 nu = 0.760000 obj = -6.964705, rho = 0.193026 nSV = 77, nBSV = 75 Total nSV = 77 Accuracy = 97% (97/100) (classification) Accuracy = 92.2% (922/1000) (classification) * optimization finished, #iter = 40 nu = 0.691510 obj = -8.470835, rho = 0.104400 nSV = 71, nBSV = 68 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 43 nu = 0.583484 obj = -10.123090, rho = 0.046627 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 34 nu = 0.485964 obj = -11.995479, rho = 0.002097 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 44 nu = 0.400721 obj = -14.163157, rho = 0.039586 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 43 nu = 0.330955 obj = -16.585052, rho = 0.001364 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 29 nu = 0.274973 obj = -19.341393, rho = -0.011102 nSV = 29, nBSV = 25 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 25 nu = 0.218536 obj = -22.346213, rho = 0.048733 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 55 nu = 0.175716 obj = -26.033456, rho = 0.086086 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 89 nu = 0.141641 obj = -30.456826, rho = 0.052691 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 80 nu = 0.115319 obj = -35.590078, rho = 0.032449 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.095041 obj = -41.521829, rho = 0.057872 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 95 nu = 0.075909 obj = -48.517332, rho = -0.010410 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.972712, rho = -0.025169 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 91% (91/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.381986, rho = -0.036204 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 91% (91/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.952307, rho = -0.052078 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 91% (91/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.734612, rho = -0.074912 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 91% (91/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.781140, rho = -0.107757 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 91% (91/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -5.123514, rho = -0.155003 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 91% (91/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -6.739587, rho = -0.177292 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 92% (92/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -8.661199, rho = -0.129026 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 93% (93/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 46 nu = 0.827483 obj = -10.865569, rho = -0.210437 nSV = 84, nBSV = 80 Total nSV = 84 Accuracy = 94% (94/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 53 nu = 0.728059 obj = -13.449410, rho = -0.183351 nSV = 74, nBSV = 71 Total nSV = 74 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 42 nu = 0.625544 obj = -16.505092, rho = -0.128215 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 74 nu = 0.539315 obj = -20.168453, rho = -0.119831 nSV = 56, nBSV = 50 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 38 nu = 0.459548 obj = -24.671557, rho = -0.141946 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 77 nu = 0.394829 obj = -30.053991, rho = -0.106968 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.328145 obj = -36.266761, rho = -0.069743 nSV = 38, nBSV = 29 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *..*.* optimization finished, #iter = 273 nu = 0.273898 obj = -44.094249, rho = -0.032910 nSV = 31, nBSV = 21 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.229945 obj = -54.259211, rho = -0.047154 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 160 nu = 0.198234 obj = -66.059934, rho = 0.012808 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 122 nu = 0.170465 obj = -80.401955, rho = 0.034942 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 164 nu = 0.140087 obj = -97.900147, rho = 0.076811 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -0.924431, rho = 0.847825 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 53.4% (534/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.307315, rho = 0.781104 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 53.4% (534/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.834090, rho = 0.685129 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 53.4% (534/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -2.542204, rho = 0.547074 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 53.4% (534/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -3.458107, rho = 0.348489 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 62% (62/100) (classification) Accuracy = 60.9% (609/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.563122, rho = 0.062834 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 87% (87/100) (classification) Accuracy = 83.9% (839/1000) (classification) * optimization finished, #iter = 50 nu = 0.895512 obj = -5.783162, rho = -0.047602 nSV = 91, nBSV = 87 Total nSV = 91 Accuracy = 95% (95/100) (classification) Accuracy = 92.3% (923/1000) (classification) * optimization finished, #iter = 45 nu = 0.804684 obj = -7.208783, rho = -0.145477 nSV = 83, nBSV = 79 Total nSV = 83 Accuracy = 97% (97/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 69 nu = 0.701711 obj = -8.869381, rho = -0.109884 nSV = 72, nBSV = 66 Total nSV = 72 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 42 nu = 0.590984 obj = -10.879331, rho = -0.088685 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 48 nu = 0.518479 obj = -13.285952, rho = 0.011253 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 32 nu = 0.444404 obj = -15.876443, rho = 0.001696 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 44 nu = 0.375165 obj = -18.633324, rho = -0.049528 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.306883 obj = -21.677817, rho = -0.029514 nSV = 33, nBSV = 29 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 75 nu = 0.249476 obj = -24.885310, rho = 0.055406 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 70 nu = 0.206148 obj = -28.180231, rho = 0.185108 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 184 nu = 0.159207 obj = -31.110370, rho = 0.189872 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 98 nu = 0.124603 obj = -34.171735, rho = 0.195035 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.096610 obj = -36.817549, rho = 0.147217 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ...*.....* optimization finished, #iter = 875 nu = 0.071330 obj = -39.118802, rho = 0.141420 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -0.954506, rho = 0.884891 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -1.356929, rho = 0.834421 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -1.918605, rho = 0.761823 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 51 nu = 0.980000 obj = -2.690978, rho = 0.657395 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 49% (490/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -3.728398, rho = 0.507180 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 51% (51/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -5.068389, rho = 0.291103 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 74% (74/100) (classification) Accuracy = 71.4% (714/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -6.680796, rho = -0.019713 nSV = 99, nBSV = 97 Total nSV = 99 Accuracy = 95% (95/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -8.479591, rho = 0.024270 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 96% (96/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -10.511367, rho = -0.024640 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 96% (96/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 47 nu = 0.695805 obj = -12.958207, rho = -0.017880 nSV = 71, nBSV = 68 Total nSV = 71 Accuracy = 96% (96/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 72 nu = 0.611515 obj = -15.894686, rho = 0.038963 nSV = 65, nBSV = 59 Total nSV = 65 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 56 nu = 0.509935 obj = -19.499864, rho = 0.057296 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 75 nu = 0.448727 obj = -23.855187, rho = 0.039497 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 49 nu = 0.385686 obj = -28.471489, rho = 0.098152 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 66 nu = 0.314721 obj = -33.737806, rho = 0.075834 nSV = 37, nBSV = 27 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 67 nu = 0.256983 obj = -40.479534, rho = 0.013041 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 64 nu = 0.215376 obj = -48.827492, rho = -0.030777 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 97 nu = 0.179612 obj = -59.122793, rho = -0.086566 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.153341 obj = -71.055768, rho = -0.079924 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 60 nu = 0.129771 obj = -85.405334, rho = 0.051936 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -0.913602, rho = -0.917052 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 47.3% (473/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.297523, rho = -0.880683 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 47.3% (473/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.831973, rho = -0.828369 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 47.3% (473/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -2.563923, rho = -0.753117 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 47.3% (473/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -3.540590, rho = -0.644872 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 47.8% (478/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.787795, rho = -0.489166 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 77% (77/100) (classification) Accuracy = 72.9% (729/1000) (classification) * optimization finished, #iter = 47 nu = 0.933845 obj = -6.256278, rho = -0.286255 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 94% (94/100) (classification) Accuracy = 94% (940/1000) (classification) * optimization finished, #iter = 47 nu = 0.842387 obj = -7.963794, rho = -0.290421 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 94% (94/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 41 nu = 0.755234 obj = -10.060684, rho = -0.225944 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 95% (95/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 39 nu = 0.675937 obj = -12.507057, rho = -0.125545 nSV = 69, nBSV = 66 Total nSV = 69 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 50 nu = 0.590384 obj = -15.270471, rho = -0.102671 nSV = 62, nBSV = 55 Total nSV = 62 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 31 nu = 0.503635 obj = -18.603851, rho = -0.125489 nSV = 52, nBSV = 49 Total nSV = 52 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 45 nu = 0.427376 obj = -22.423358, rho = -0.127360 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 55 nu = 0.354605 obj = -26.961091, rho = -0.153518 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.295237 obj = -32.467896, rho = -0.214210 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 123 nu = 0.242025 obj = -39.612838, rho = -0.245794 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 57 nu = 0.205175 obj = -48.981376, rho = -0.202385 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 96% (96/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 112 nu = 0.177541 obj = -60.462260, rho = -0.349000 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.151830 obj = -75.028824, rho = -0.336975 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 60 nu = 0.128872 obj = -93.985224, rho = -0.418632 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -0.955663, rho = -0.904832 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.359324, rho = -0.863105 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -1.923559, rho = -0.803084 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -2.701229, rho = -0.716746 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 51.2% (512/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -3.749608, rho = -0.592553 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 52.3% (523/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -5.112276, rho = -0.413908 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 73% (73/100) (classification) Accuracy = 79.7% (797/1000) (classification) * optimization finished, #iter = 50 nu = 0.980000 obj = -6.771604, rho = -0.156936 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 94% (94/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 49 nu = 0.914021 obj = -8.686851, rho = -0.065903 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 95% (95/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 46 nu = 0.822330 obj = -10.926778, rho = 0.045656 nSV = 84, nBSV = 80 Total nSV = 84 Accuracy = 98% (98/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 40 nu = 0.720000 obj = -13.707789, rho = 0.052001 nSV = 74, nBSV = 71 Total nSV = 74 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 61 nu = 0.632409 obj = -17.092548, rho = 0.053511 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 40 nu = 0.552946 obj = -21.173640, rho = 0.061898 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 41 nu = 0.474192 obj = -26.181611, rho = 0.091584 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 98 nu = 0.402897 obj = -32.318981, rho = -0.033441 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 80 nu = 0.344348 obj = -40.084166, rho = -0.097509 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 38 nu = 0.303645 obj = -50.083849, rho = -0.112277 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.264264 obj = -61.606131, rho = 0.005078 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 92 nu = 0.223623 obj = -75.769593, rho = 0.029415 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *..* optimization finished, #iter = 288 nu = 0.187304 obj = -93.688732, rho = 0.009447 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.164799 obj = -117.534602, rho = 0.193582 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -0.969722, rho = -0.029407 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.375800, rho = -0.042301 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -1.939507, rho = -0.060848 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -2.708127, rho = -0.087527 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -3.726339, rho = -0.125902 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 50 nu = 1.000000 obj = -5.010125, rho = -0.181104 nSV = 100, nBSV = 100 Total nSV = 100 Accuracy = 94% (94/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -6.510399, rho = -0.163767 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 49 nu = 0.903273 obj = -8.150232, rho = -0.089751 nSV = 94, nBSV = 90 Total nSV = 94 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 45 nu = 0.793139 obj = -9.987883, rho = -0.128697 nSV = 81, nBSV = 77 Total nSV = 81 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.674660 obj = -12.201130, rho = -0.122170 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 38 nu = 0.580487 obj = -14.829659, rho = -0.105678 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.488251 obj = -17.896439, rho = -0.180799 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 75 nu = 0.414249 obj = -21.265155, rho = -0.249366 nSV = 44, nBSV = 36 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 58 nu = 0.343513 obj = -25.284993, rho = -0.282927 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 64 nu = 0.283394 obj = -29.866398, rho = -0.284699 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 39 nu = 0.238230 obj = -35.215208, rho = -0.256440 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.194575 obj = -40.640598, rho = -0.215435 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 87 nu = 0.155982 obj = -46.683855, rho = -0.268200 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 82 nu = 0.133416 obj = -52.567545, rho = -0.190136 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 63 nu = 0.100112 obj = -57.344541, rho = -0.160318 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.950057, rho = 0.854549 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.347724, rho = 0.790775 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.899559, rho = 0.699041 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.651568, rho = 0.567085 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.646854, rho = 0.377274 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 58% (58/100) (classification) Accuracy = 54.2% (542/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.899663, rho = 0.104240 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 83% (83/100) (classification) Accuracy = 84.7% (847/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.377380, rho = -0.071337 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 99% (99/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 47 nu = 0.865394 obj = -8.147792, rho = -0.048175 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 42 nu = 0.774609 obj = -10.242057, rho = 0.006692 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 55 nu = 0.696867 obj = -12.620374, rho = 0.071773 nSV = 72, nBSV = 67 Total nSV = 72 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 40 nu = 0.596577 obj = -15.374618, rho = 0.041702 nSV = 61, nBSV = 58 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 41 nu = 0.511156 obj = -18.490495, rho = 0.061848 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 45 nu = 0.437828 obj = -21.873450, rho = -0.012438 nSV = 47, nBSV = 40 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *..* optimization finished, #iter = 216 nu = 0.357045 obj = -25.405068, rho = 0.053999 nSV = 42, nBSV = 32 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 67 nu = 0.286094 obj = -29.646538, rho = -0.017533 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 182 nu = 0.228352 obj = -34.908235, rho = 0.013804 nSV = 27, nBSV = 17 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 62 nu = 0.185109 obj = -41.724548, rho = 0.011145 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 286 nu = 0.151057 obj = -50.715885, rho = 0.010486 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 79 nu = 0.132907 obj = -61.542196, rho = 0.032814 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.113482 obj = -73.306413, rho = -0.089828 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 41 nu = 0.740000 obj = -0.727351, rho = 0.954007 nSV = 75, nBSV = 73 Total nSV = 75 Accuracy = 63% (63/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 41 nu = 0.740000 obj = -1.038281, rho = 0.933841 nSV = 75, nBSV = 73 Total nSV = 75 Accuracy = 63% (63/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 41 nu = 0.740000 obj = -1.477008, rho = 0.904834 nSV = 75, nBSV = 73 Total nSV = 75 Accuracy = 63% (63/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 41 nu = 0.740000 obj = -2.090447, rho = 0.863108 nSV = 75, nBSV = 73 Total nSV = 75 Accuracy = 63% (63/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 41 nu = 0.740000 obj = -2.936333, rho = 0.803088 nSV = 75, nBSV = 73 Total nSV = 75 Accuracy = 63% (63/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 40 nu = 0.740000 obj = -4.077542, rho = 0.716752 nSV = 75, nBSV = 73 Total nSV = 75 Accuracy = 63% (63/100) (classification) Accuracy = 50.9% (509/1000) (classification) * optimization finished, #iter = 40 nu = 0.740000 obj = -5.562779, rho = 0.592214 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 64% (64/100) (classification) Accuracy = 52.5% (525/1000) (classification) * optimization finished, #iter = 40 nu = 0.740000 obj = -7.375736, rho = 0.413420 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 87% (87/100) (classification) Accuracy = 77.4% (774/1000) (classification) * optimization finished, #iter = 39 nu = 0.720000 obj = -9.336442, rho = 0.218823 nSV = 73, nBSV = 70 Total nSV = 73 Accuracy = 97% (97/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 38 nu = 0.649456 obj = -11.429789, rho = 0.127967 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 45 nu = 0.549886 obj = -13.724038, rho = 0.119157 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 56 nu = 0.457793 obj = -16.346785, rho = 0.106838 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 41 nu = 0.374440 obj = -19.456330, rho = 0.131459 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 65 nu = 0.313381 obj = -23.239056, rho = 0.103062 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.255031 obj = -27.728655, rho = 0.136471 nSV = 30, nBSV = 21 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 51 nu = 0.218180 obj = -33.204081, rho = -0.014826 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 165 nu = 0.177319 obj = -39.564571, rho = -0.033507 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 195 nu = 0.144854 obj = -47.548516, rho = -0.021549 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 76 nu = 0.121915 obj = -57.674588, rho = 0.038075 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 78 nu = 0.105009 obj = -69.166920, rho = 0.172991 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -0.908699, rho = -0.925305 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.287376, rho = -0.892555 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -1.810979, rho = -0.845445 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -2.520483, rho = -0.777681 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 50.6% (506/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -3.450706, rho = -0.680205 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 66% (66/100) (classification) Accuracy = 56% (560/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -4.601812, rho = -0.539991 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 89% (89/100) (classification) Accuracy = 79.9% (799/1000) (classification) * optimization finished, #iter = 47 nu = 0.893194 obj = -5.907426, rho = -0.453509 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 94% (94/100) (classification) Accuracy = 90.2% (902/1000) (classification) * optimization finished, #iter = 44 nu = 0.796505 obj = -7.522837, rho = -0.486609 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 94% (94/100) (classification) Accuracy = 91.7% (917/1000) (classification) * optimization finished, #iter = 38 nu = 0.700000 obj = -9.535217, rho = -0.485286 nSV = 71, nBSV = 69 Total nSV = 71 Accuracy = 94% (94/100) (classification) Accuracy = 93.5% (935/1000) (classification) * optimization finished, #iter = 35 nu = 0.620000 obj = -12.091433, rho = -0.450382 nSV = 63, nBSV = 61 Total nSV = 63 Accuracy = 95% (95/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 36 nu = 0.554560 obj = -15.208152, rho = -0.399719 nSV = 57, nBSV = 54 Total nSV = 57 Accuracy = 95% (95/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 34 nu = 0.486877 obj = -18.958221, rho = -0.338261 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 94% (94/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 38 nu = 0.421066 obj = -23.612458, rho = -0.320994 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 94% (94/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 23 nu = 0.360000 obj = -29.590048, rho = -0.355565 nSV = 37, nBSV = 35 Total nSV = 37 Accuracy = 94% (94/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 36 nu = 0.314995 obj = -36.984584, rho = -0.464847 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 94% (94/100) (classification) Accuracy = 95.9% (959/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.273556 obj = -46.436502, rho = -0.470742 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 95% (95/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.235520 obj = -58.392769, rho = -0.497108 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 96% (96/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 78 nu = 0.204844 obj = -74.403826, rho = -0.469419 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 96% (96/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.177934 obj = -95.013178, rho = -0.529870 nSV = 26, nBSV = 14 Total nSV = 26 Accuracy = 96% (96/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 142 nu = 0.156581 obj = -123.039051, rho = -0.631558 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 96% (96/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -0.863048, rho = 0.916695 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -1.230759, rho = 0.880170 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 56% (56/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -1.748266, rho = 0.827680 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 56% (56/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -2.469020, rho = 0.752127 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 56% (56/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -3.456852, rho = 0.643447 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 56% (56/100) (classification) Accuracy = 48.8% (488/1000) (classification) * optimization finished, #iter = 48 nu = 0.880000 obj = -4.776542, rho = 0.486410 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 57% (57/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 48 nu = 0.880000 obj = -6.465333, rho = 0.260745 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 75% (75/100) (classification) Accuracy = 73.7% (737/1000) (classification) * optimization finished, #iter = 44 nu = 0.863656 obj = -8.468030, rho = 0.016494 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 92% (92/100) (classification) Accuracy = 93.4% (934/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -10.728143, rho = -0.012006 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 45 nu = 0.713154 obj = -13.280241, rho = -0.032854 nSV = 73, nBSV = 69 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 46 nu = 0.632103 obj = -16.256561, rho = 0.046091 nSV = 64, nBSV = 61 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 57 nu = 0.525691 obj = -19.745600, rho = 0.034961 nSV = 56, nBSV = 50 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 72 nu = 0.442806 obj = -24.050795, rho = 0.058901 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.378799 obj = -29.528863, rho = 0.068718 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 78 nu = 0.326039 obj = -35.641763, rho = -0.085735 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 55 nu = 0.271088 obj = -42.997663, rho = -0.089553 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 49 nu = 0.234820 obj = -51.419298, rho = -0.072688 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 51 nu = 0.196255 obj = -60.603635, rho = -0.090934 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.160225 obj = -70.222893, rho = -0.201670 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 245 nu = 0.130764 obj = -81.638970, rho = -0.158559 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 39 nu = 0.740000 obj = -0.730153, rho = -0.964494 nSV = 75, nBSV = 73 Total nSV = 75 Accuracy = 63% (63/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 39 nu = 0.740000 obj = -1.044078, rho = -0.948927 nSV = 75, nBSV = 73 Total nSV = 75 Accuracy = 63% (63/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 39 nu = 0.740000 obj = -1.489004, rho = -0.926534 nSV = 75, nBSV = 73 Total nSV = 75 Accuracy = 63% (63/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 40 nu = 0.740000 obj = -2.115271, rho = -0.895138 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 63% (63/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 42 nu = 0.740000 obj = -2.987708, rho = -0.849188 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 63% (63/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 42 nu = 0.740000 obj = -4.183842, rho = -0.783065 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 63% (63/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 44 nu = 0.740000 obj = -5.782728, rho = -0.687305 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 63% (63/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 44 nu = 0.740000 obj = -7.830840, rho = -0.550205 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 74% (74/100) (classification) Accuracy = 71.1% (711/1000) (classification) * optimization finished, #iter = 46 nu = 0.740000 obj = -10.255938, rho = -0.354099 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 96% (96/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 45 nu = 0.684722 obj = -12.896565, rho = -0.233465 nSV = 70, nBSV = 67 Total nSV = 70 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 35 nu = 0.612319 obj = -15.903593, rho = -0.218980 nSV = 62, nBSV = 60 Total nSV = 62 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 75 nu = 0.525776 obj = -19.086917, rho = -0.171193 nSV = 57, nBSV = 50 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 62 nu = 0.436176 obj = -22.808597, rho = -0.172501 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 77 nu = 0.357303 obj = -27.424617, rho = -0.149829 nSV = 41, nBSV = 33 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 42 nu = 0.310951 obj = -32.732106, rho = -0.092814 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 42 nu = 0.254087 obj = -38.509843, rho = -0.200316 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 53 nu = 0.210332 obj = -45.298267, rho = -0.260235 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.173449 obj = -52.418242, rho = -0.171873 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 142 nu = 0.141102 obj = -60.180191, rho = -0.167440 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 163 nu = 0.110818 obj = -69.679989, rho = -0.195214 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -0.880912, rho = 0.925332 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 47.8% (478/1000) (classification) * optimization finished, #iter = 49 nu = 0.900000 obj = -1.255113, rho = 0.892890 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 55% (55/100) (classification) Accuracy = 47.8% (478/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -1.780510, rho = 0.845928 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 55% (55/100) (classification) Accuracy = 47.8% (478/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -2.509639, rho = 0.778376 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 55% (55/100) (classification) Accuracy = 47.8% (478/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -3.503355, rho = 0.681204 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 55% (55/100) (classification) Accuracy = 47.8% (478/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -4.818758, rho = 0.541222 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 55% (55/100) (classification) Accuracy = 48.9% (489/1000) (classification) * optimization finished, #iter = 49 nu = 0.900000 obj = -6.475004, rho = 0.339699 nSV = 92, nBSV = 88 Total nSV = 92 Accuracy = 78% (78/100) (classification) Accuracy = 78.4% (784/1000) (classification) * optimization finished, #iter = 49 nu = 0.899115 obj = -8.369349, rho = 0.053482 nSV = 91, nBSV = 87 Total nSV = 91 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 67 nu = 0.798818 obj = -10.498174, rho = 0.061618 nSV = 83, nBSV = 77 Total nSV = 83 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 36 nu = 0.720000 obj = -12.946215, rho = -0.029701 nSV = 72, nBSV = 72 Total nSV = 72 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 41 nu = 0.612840 obj = -15.670022, rho = -0.011139 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 32 nu = 0.523567 obj = -18.879018, rho = -0.096782 nSV = 54, nBSV = 51 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 39 nu = 0.435886 obj = -22.435062, rho = -0.069881 nSV = 47, nBSV = 40 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 41 nu = 0.362030 obj = -26.788347, rho = -0.101710 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.298808 obj = -31.669800, rho = -0.077539 nSV = 34, nBSV = 25 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 75 nu = 0.247703 obj = -37.533418, rho = -0.114601 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 97 nu = 0.209272 obj = -43.630255, rho = -0.058529 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 193 nu = 0.164865 obj = -50.532063, rho = -0.061456 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.134569 obj = -58.680877, rho = -0.085968 nSV = 19, nBSV = 8 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 224 nu = 0.111136 obj = -67.251372, rho = -0.101022 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.948833, rho = 0.868547 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.345192, rho = 0.810911 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.894319, rho = 0.728005 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.640726, rho = 0.608749 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.3% (493/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.624420, rho = 0.437205 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 55% (55/100) (classification) Accuracy = 54% (540/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -4.853243, rho = 0.190448 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 89% (89/100) (classification) Accuracy = 89.3% (893/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -6.243627, rho = -0.061350 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -7.797759, rho = -0.044908 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 42 nu = 0.751033 obj = -9.630285, rho = -0.078509 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 35 nu = 0.660000 obj = -11.768170, rho = -0.054643 nSV = 67, nBSV = 64 Total nSV = 67 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 30 nu = 0.560000 obj = -14.180120, rho = 0.008397 nSV = 57, nBSV = 55 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 42 nu = 0.467604 obj = -16.954319, rho = -0.012176 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 40 nu = 0.391640 obj = -20.372185, rho = -0.080553 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 46 nu = 0.326415 obj = -24.331157, rho = -0.012889 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 192 nu = 0.276746 obj = -28.733212, rho = 0.024129 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 76 nu = 0.233985 obj = -33.391576, rho = 0.048348 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 51 nu = 0.186614 obj = -38.183206, rho = 0.113185 nSV = 20, nBSV = 15 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 82 nu = 0.147529 obj = -43.222921, rho = 0.081209 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 69 nu = 0.114543 obj = -49.402868, rho = 0.067685 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.099237 obj = -56.146452, rho = 0.255383 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -0.932208, rho = 0.862047 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.323407, rho = 0.801562 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -1.867387, rho = 0.714556 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -2.611100, rho = 0.589404 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 52% (52/100) (classification) Accuracy = 50.4% (504/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -3.600663, rho = 0.409378 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 54% (54/100) (classification) Accuracy = 51.5% (515/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -4.858091, rho = 0.150419 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 81% (81/100) (classification) Accuracy = 78.6% (786/1000) (classification) * optimization finished, #iter = 49 nu = 0.945462 obj = -6.326212, rho = -0.159915 nSV = 96, nBSV = 93 Total nSV = 96 Accuracy = 97% (97/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 44 nu = 0.860980 obj = -8.021736, rho = -0.127672 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 46 nu = 0.770725 obj = -9.984848, rho = -0.123156 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.666708 obj = -12.337893, rho = -0.109368 nSV = 68, nBSV = 63 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 64 nu = 0.576536 obj = -15.131440, rho = -0.111314 nSV = 62, nBSV = 55 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 148 nu = 0.495957 obj = -18.476485, rho = -0.049718 nSV = 54, nBSV = 46 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 37 nu = 0.428896 obj = -22.370278, rho = -0.046003 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 47 nu = 0.362051 obj = -26.679754, rho = -0.102279 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 43 nu = 0.300591 obj = -31.765105, rho = -0.156893 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.247075 obj = -37.346320, rho = -0.206418 nSV = 30, nBSV = 21 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.199064 obj = -44.218104, rho = -0.238550 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.176376 obj = -52.396835, rho = -0.157913 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 181 nu = 0.139714 obj = -60.009273, rho = -0.217860 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 250 nu = 0.112110 obj = -68.568321, rho = -0.209912 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -0.918346, rho = -0.939172 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.307338, rho = -0.912502 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -1.852283, rho = -0.874138 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -2.605947, rho = -0.818954 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -3.627544, rho = -0.739574 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 53% (53/100) (classification) Accuracy = 48.2% (482/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -4.967715, rho = -0.625390 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 62% (62/100) (classification) Accuracy = 58.1% (581/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -6.627851, rho = -0.461143 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 89% (89/100) (classification) Accuracy = 85.1% (851/1000) (classification) * optimization finished, #iter = 50 nu = 0.900197 obj = -8.507180, rho = -0.303416 nSV = 93, nBSV = 89 Total nSV = 93 Accuracy = 96% (96/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 43 nu = 0.830947 obj = -10.669579, rho = -0.373770 nSV = 84, nBSV = 82 Total nSV = 84 Accuracy = 96% (96/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 42 nu = 0.713333 obj = -13.060926, rho = -0.395197 nSV = 73, nBSV = 69 Total nSV = 73 Accuracy = 97% (97/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 42 nu = 0.627238 obj = -15.882976, rho = -0.310764 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 44 nu = 0.531988 obj = -19.089689, rho = -0.264512 nSV = 55, nBSV = 52 Total nSV = 55 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 58 nu = 0.441807 obj = -22.687119, rho = -0.226648 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.370386 obj = -26.828464, rho = -0.196944 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 71 nu = 0.297023 obj = -31.845857, rho = -0.196928 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 94 nu = 0.249868 obj = -37.640063, rho = -0.323592 nSV = 30, nBSV = 21 Total nSV = 30 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 192 nu = 0.204144 obj = -44.301976, rho = -0.347863 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.162642 obj = -52.565008, rho = -0.342370 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 288 nu = 0.137562 obj = -62.906095, rho = -0.330724 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.111529 obj = -75.548792, rho = -0.355798 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 50 nu = 0.940000 obj = -0.912498, rho = 0.876837 nSV = 96, nBSV = 93 Total nSV = 96 Accuracy = 53% (53/100) (classification) Accuracy = 46.3% (463/1000) (classification) * optimization finished, #iter = 51 nu = 0.940000 obj = -1.295243, rho = 0.822732 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 46.3% (463/1000) (classification) * optimization finished, #iter = 52 nu = 0.940000 obj = -1.827256, rho = 0.745659 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 46.3% (463/1000) (classification) * optimization finished, #iter = 52 nu = 0.940000 obj = -2.554164, rho = 0.634143 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 53% (53/100) (classification) Accuracy = 46.3% (463/1000) (classification) * optimization finished, #iter = 52 nu = 0.940000 obj = -3.520397, rho = 0.473734 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 55% (55/100) (classification) Accuracy = 46.8% (468/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -4.746013, rho = 0.242992 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 80% (80/100) (classification) Accuracy = 72.1% (721/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -6.177288, rho = -0.006977 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 96% (96/100) (classification) Accuracy = 93.3% (933/1000) (classification) * optimization finished, #iter = 45 nu = 0.856470 obj = -7.811474, rho = -0.069653 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 48 nu = 0.767542 obj = -9.606830, rho = -0.099952 nSV = 78, nBSV = 74 Total nSV = 78 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 60 nu = 0.651147 obj = -11.622344, rho = -0.085415 nSV = 68, nBSV = 62 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 51 nu = 0.551095 obj = -14.031409, rho = -0.130671 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 51 nu = 0.468349 obj = -16.796290, rho = -0.115084 nSV = 50, nBSV = 43 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 52 nu = 0.385253 obj = -20.027475, rho = -0.169790 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 59 nu = 0.318238 obj = -23.961983, rho = -0.158407 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 172 nu = 0.270131 obj = -28.552935, rho = -0.237248 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 43 nu = 0.219761 obj = -33.943708, rho = -0.244073 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 42 nu = 0.185876 obj = -40.362854, rho = -0.171645 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 74 nu = 0.155367 obj = -47.631291, rho = -0.235193 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 51 nu = 0.127825 obj = -55.434031, rho = -0.529596 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 81 nu = 0.107082 obj = -62.856739, rho = -0.770563 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -0.898433, rho = 0.911021 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.278749, rho = 0.872008 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -1.811271, rho = 0.815889 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -2.547188, rho = 0.735166 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -3.543505, rho = 0.619050 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 54% (54/100) (classification) Accuracy = 51.3% (513/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -4.847831, rho = 0.452022 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 59% (59/100) (classification) Accuracy = 57.4% (574/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -6.457476, rho = 0.211762 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 86% (86/100) (classification) Accuracy = 87% (870/1000) (classification) * optimization finished, #iter = 48 nu = 0.866284 obj = -8.316438, rho = 0.142783 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 90% (90/100) (classification) Accuracy = 93.6% (936/1000) (classification) * optimization finished, #iter = 42 nu = 0.800000 obj = -10.506800, rho = 0.029010 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 96% (96/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 48 nu = 0.715452 obj = -13.011540, rho = -0.025053 nSV = 73, nBSV = 68 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 41 nu = 0.603376 obj = -15.953339, rho = -0.076006 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 32 nu = 0.522417 obj = -19.512039, rho = -0.120932 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 56 nu = 0.433594 obj = -23.814795, rho = -0.119092 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 98 nu = 0.369637 obj = -29.322228, rho = -0.131199 nSV = 41, nBSV = 33 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 64 nu = 0.308542 obj = -36.510896, rho = -0.157022 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 30 nu = 0.265984 obj = -45.951164, rho = -0.142811 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 36 nu = 0.235619 obj = -58.081128, rho = -0.086407 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 87 nu = 0.206416 obj = -73.305628, rho = -0.119491 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 96% (96/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.187105 obj = -91.486676, rho = -0.242559 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) .*.* optimization finished, #iter = 230 nu = 0.163722 obj = -112.458112, rho = -0.241630 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -0.898915, rho = -0.922398 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -1.279748, rho = -0.889144 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -1.813340, rho = -0.840984 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -2.551468, rho = -0.771263 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -3.552361, rho = -0.670974 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 54% (54/100) (classification) Accuracy = 51.4% (514/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -4.866155, rho = -0.526712 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 56% (56/100) (classification) Accuracy = 55.4% (554/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -6.495391, rho = -0.319199 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 81% (81/100) (classification) Accuracy = 81.6% (816/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -8.350863, rho = -0.085607 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 99% (99/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 54 nu = 0.811838 obj = -10.387888, rho = 0.001730 nSV = 84, nBSV = 80 Total nSV = 84 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 41 nu = 0.716028 obj = -12.620212, rho = 0.069968 nSV = 72, nBSV = 69 Total nSV = 72 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 61 nu = 0.607836 obj = -15.050931, rho = 0.097921 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 55 nu = 0.513589 obj = -17.706619, rho = 0.009715 nSV = 54, nBSV = 47 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 60 nu = 0.410257 obj = -20.718037, rho = -0.029944 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 28 nu = 0.335120 obj = -24.434874, rho = -0.084944 nSV = 35, nBSV = 32 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 138 nu = 0.276340 obj = -28.518312, rho = -0.108947 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.229576 obj = -32.994921, rho = -0.048105 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.182977 obj = -37.782161, rho = -0.080400 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 93 nu = 0.148279 obj = -43.088392, rho = -0.066592 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*....* optimization finished, #iter = 589 nu = 0.124560 obj = -47.480092, rho = 0.042101 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ...* optimization finished, #iter = 331 nu = 0.092142 obj = -50.875727, rho = 0.042287 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 42 nu = 0.800000 obj = -0.785599, rho = -0.953081 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 42 nu = 0.800000 obj = -1.120963, rho = -0.932510 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 42 nu = 0.800000 obj = -1.593656, rho = -0.902918 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -2.253513, rho = -0.859195 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -3.161110, rho = -0.797460 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -4.380625, rho = -0.708656 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 60% (60/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 44 nu = 0.800000 obj = -5.956852, rho = -0.581336 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 67% (67/100) (classification) Accuracy = 57.6% (576/1000) (classification) * optimization finished, #iter = 44 nu = 0.800000 obj = -7.855904, rho = -0.397772 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 87% (87/100) (classification) Accuracy = 88% (880/1000) (classification) * optimization finished, #iter = 42 nu = 0.760000 obj = -9.922366, rho = -0.203160 nSV = 77, nBSV = 74 Total nSV = 77 Accuracy = 95% (95/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 44 nu = 0.664357 obj = -12.241321, rho = -0.184038 nSV = 68, nBSV = 63 Total nSV = 68 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 39 nu = 0.580686 obj = -14.972975, rho = -0.112512 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 36 nu = 0.498339 obj = -18.123189, rho = -0.109440 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.413757 obj = -21.673886, rho = -0.195253 nSV = 46, nBSV = 38 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 168 nu = 0.344099 obj = -26.069062, rho = -0.190497 nSV = 38, nBSV = 28 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 75 nu = 0.282453 obj = -31.567978, rho = -0.162066 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 67 nu = 0.238692 obj = -38.562296, rho = -0.107457 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 82 nu = 0.205589 obj = -47.121029, rho = -0.127712 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 185 nu = 0.174068 obj = -56.558689, rho = -0.103528 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 78 nu = 0.143282 obj = -68.816735, rho = -0.160169 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 82 nu = 0.123828 obj = -83.274819, rho = -0.231109 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -0.953843, rho = 0.862202 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.355559, rho = 0.801785 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -1.915769, rho = 0.714878 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -2.685109, rho = 0.589866 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.2% (492/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -3.716255, rho = 0.410043 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 51% (51/100) (classification) Accuracy = 49.9% (499/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -5.043263, rho = 0.151376 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 77% (77/100) (classification) Accuracy = 75% (750/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -6.628808, rho = -0.220703 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 98% (98/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -8.467705, rho = -0.191793 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 45 nu = 0.829718 obj = -10.560677, rho = -0.164453 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 40 nu = 0.714287 obj = -12.866384, rho = -0.140802 nSV = 73, nBSV = 69 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 99.4% (994/1000) (classification) * optimization finished, #iter = 42 nu = 0.609002 obj = -15.551482, rho = -0.094112 nSV = 65, nBSV = 59 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 32 nu = 0.507860 obj = -18.791085, rho = -0.089973 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 58 nu = 0.431329 obj = -22.613160, rho = -0.041355 nSV = 46, nBSV = 38 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 38 nu = 0.362571 obj = -27.333235, rho = -0.072940 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 57 nu = 0.308291 obj = -32.600909, rho = 0.003714 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 79 nu = 0.251933 obj = -38.794611, rho = -0.068489 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 39 nu = 0.209394 obj = -46.476157, rho = -0.090183 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 85 nu = 0.177713 obj = -54.345294, rho = -0.087069 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.143494 obj = -63.499545, rho = -0.112913 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) ..* optimization finished, #iter = 264 nu = 0.114925 obj = -74.203475, rho = -0.189430 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -0.857465, rho = 0.886498 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -1.219209, rho = 0.836733 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -1.724364, rho = 0.765149 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -2.419563, rho = 0.662178 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.7% (507/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -3.354519, rho = 0.514060 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 56% (56/100) (classification) Accuracy = 50.8% (508/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -4.564799, rho = 0.301000 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 68% (68/100) (classification) Accuracy = 59.1% (591/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -6.027208, rho = -0.005477 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 89% (89/100) (classification) Accuracy = 85.7% (857/1000) (classification) * optimization finished, #iter = 47 nu = 0.828213 obj = -7.621354, rho = -0.203238 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 96% (96/100) (classification) Accuracy = 93.2% (932/1000) (classification) * optimization finished, #iter = 40 nu = 0.746412 obj = -9.437120, rho = -0.138076 nSV = 76, nBSV = 74 Total nSV = 76 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 43 nu = 0.641738 obj = -11.423473, rho = -0.139548 nSV = 66, nBSV = 64 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 57 nu = 0.538614 obj = -13.801415, rho = -0.131487 nSV = 58, nBSV = 51 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.457342 obj = -16.681483, rho = -0.146235 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 44 nu = 0.388795 obj = -19.950002, rho = -0.136707 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 59 nu = 0.322960 obj = -23.471937, rho = -0.235011 nSV = 37, nBSV = 27 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 37 nu = 0.265134 obj = -27.677831, rho = -0.231694 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.213763 obj = -32.572482, rho = -0.247475 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 48 nu = 0.176327 obj = -38.495842, rho = -0.261304 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 55 nu = 0.146467 obj = -45.503722, rho = -0.288444 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.116014 obj = -54.119686, rho = -0.271021 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 74 nu = 0.099946 obj = -64.715162, rho = -0.416000 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification)
In [ ]:
import numpy as np
import numpy.matlib as matlib
from libsvm.svmutil import *
import matplotlib.pyplot as plt
def data(N,sigma):
w = np.ones(10)/np.sqrt(10)
w1 = [1., 1., 1., 1., 1., -1., -1., -1., -1., -1.]/np.sqrt(10)
w2 = [-1., -1., 0, 1., 1., -1., -1., 0, -1., -1.]/np.sqrt(8)
x = np.zeros((4,10))
x[1,:] = x[0,:] + sigma*w1
x[2,:] = x[0,:] + sigma*w2
x[3,:] = x[2,:] + sigma*w1
X1 = x + sigma*matlib.repmat(w,4,1)/2
X2 = x - sigma*matlib.repmat(w,4,1)/2
X1 = matlib.repmat(X1,2*N,1)
X2 = matlib.repmat(X2,2*N,1)
X = np.concatenate((X1, X2), axis=0)
Y = np.concatenate((np.ones(4*2*N), -np.ones(4*2*N)),axis=0)
Z = np.random.permutation(16*N)
Z = Z[:N]
X = X[Z,:]
X = X + 0.2*sigma*np.random.randn(N,10)
Y = Y[Z]
return X, Y
# Task 2a: Generating Parameter Values
lambda_values = np.logspace(-2, 1, 20) # Logarithmically spaced values between 0.01 and 10
# Initialize arrays to store errors
training_errors = []
test_errors = []
sigma = 3
# Task 2b-d: Training, Testing, and Repeating the Experiment
# num_iterations = 100
for i in range(num_iterations):
# Generate data
X_train, y_train = data(100,sigma)
X_test, y_test = data(1000, sigma)
for lam in lambda_values:
# Train SVM
svm_problem_setup = svm_problem(y_train.tolist(), X_train.tolist())
param = svm_parameter(f'-t 0 -c {lam}')
model = svm_train(svm_problem_setup, param)
# Predict on training and test data
i, train_accuracy, i = svm_predict(y_train.tolist(), X_train.tolist(), model)
i, test_accuracy, i = svm_predict(y_test.tolist(), X_test.tolist(), model)
# Calculate errors
training_errors.append(100 - train_accuracy[0]) # Convert to error percentage
test_errors.append(100 - test_accuracy[0]) # Convert to error percentage
# Task 2e: Averaging Errors and Plotting
training_errors = np.array(training_errors).reshape(num_iterations, -1)
test_errors = np.array(test_errors).reshape(num_iterations, -1)
avg_training_error = np.mean(training_errors, axis=0)
avg_test_error = np.mean(test_errors, axis=0)
lambda_values_log = np.log10(lambda_values)
# Plotting
plt.figure(figsize=(10, 6))
plt.plot(lambda_values_log, avg_training_error, label='R_empirical (Average Training Error)')
plt.plot(lambda_values_log, avg_test_error, label='R_actual (Average Test Error)')
plt.plot(lambda_values_log, avg_test_error - avg_training_error, label='R_structural (Difference)')
plt.xlabel('log(λ)')
plt.ylabel('Error (%)')
plt.title('Risks vs. λ (0.01,10) @ σ = 3')
plt.legend()
plt.show()
* optimization finished, #iter = 39 nu = 0.576530 obj = -0.380738, rho = -0.186063 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 52 nu = 0.468826 obj = -0.456413, rho = -0.252658 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 34 nu = 0.400000 obj = -0.551934, rho = -0.248054 nSV = 41, nBSV = 38 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 35 nu = 0.344768 obj = -0.653207, rho = -0.120397 nSV = 37, nBSV = 33 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 44 nu = 0.280534 obj = -0.762595, rho = -0.133010 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 41 nu = 0.224157 obj = -0.894680, rho = -0.152303 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.188049 obj = -1.040691, rho = -0.003802 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 92 nu = 0.149157 obj = -1.211012, rho = -0.044550 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 72 nu = 0.121056 obj = -1.417628, rho = -0.089917 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.098831 obj = -1.654418, rho = -0.176546 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 55 nu = 0.079950 obj = -1.924867, rho = -0.201699 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 74 nu = 0.067801 obj = -2.198938, rho = -0.269724 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 69 nu = 0.053828 obj = -2.473454, rho = -0.443824 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.040549 obj = -2.776568, rho = -0.477961 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 82 nu = 0.032920 obj = -3.107947, rho = -0.707736 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 99 nu = 0.028139 obj = -3.349394, rho = -0.964501 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.019944 obj = -3.351027, rho = -0.988347 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.013865 obj = -3.351027, rho = -0.988347 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.009639 obj = -3.351027, rho = -0.988347 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.006701 obj = -3.351027, rho = -0.988347 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.643056 obj = -0.453552, rho = -0.132182 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 49 nu = 0.560649 obj = -0.557408, rho = -0.163104 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 72 nu = 0.477947 obj = -0.674138, rho = -0.139837 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 37 nu = 0.399071 obj = -0.818189, rho = -0.168112 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 85 nu = 0.347049 obj = -0.984933, rho = -0.093166 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 66 nu = 0.286316 obj = -1.178063, rho = -0.153143 nSV = 33, nBSV = 23 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *..* optimization finished, #iter = 268 nu = 0.233505 obj = -1.427395, rho = -0.151934 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 55 nu = 0.195211 obj = -1.750069, rho = -0.200979 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.166340 obj = -2.167855, rho = -0.200985 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 60 nu = 0.146797 obj = -2.666572, rho = -0.332962 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 185 nu = 0.125493 obj = -3.232415, rho = -0.329829 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 224 nu = 0.105646 obj = -3.934392, rho = -0.372136 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..*.* optimization finished, #iter = 328 nu = 0.090376 obj = -4.736589, rho = -0.443849 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..*.* optimization finished, #iter = 396 nu = 0.072122 obj = -5.777105, rho = -0.444015 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 177 nu = 0.061154 obj = -7.230499, rho = -0.506814 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 153 nu = 0.054119 obj = -9.058336, rho = -0.584263 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.046707 obj = -11.289067, rho = -0.670096 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 168 nu = 0.045317 obj = -13.570046, rho = -1.341721 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 230 nu = 0.039465 obj = -15.122191, rho = -2.322718 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) ..*.* optimization finished, #iter = 365 nu = 0.030758 obj = -15.384385, rho = -2.927288 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 43 nu = 0.544525 obj = -0.366421, rho = 0.073823 nSV = 58, nBSV = 51 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 39 nu = 0.452980 obj = -0.444144, rho = 0.106953 nSV = 47, nBSV = 44 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 25 nu = 0.386129 obj = -0.538560, rho = 0.106890 nSV = 40, nBSV = 38 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 29 nu = 0.327333 obj = -0.645472, rho = 0.113558 nSV = 34, nBSV = 30 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 25 nu = 0.272803 obj = -0.770530, rho = 0.103517 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 60 nu = 0.232287 obj = -0.900773, rho = -0.000910 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *..* optimization finished, #iter = 290 nu = 0.182434 obj = -1.054258, rho = 0.016440 nSV = 24, nBSV = 12 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.152324 obj = -1.246211, rho = 0.123407 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 99 nu = 0.123626 obj = -1.469840, rho = 0.023906 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 99 nu = 0.105120 obj = -1.712550, rho = -0.002827 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 171 nu = 0.084754 obj = -1.958905, rho = -0.040937 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 71 nu = 0.069559 obj = -2.188916, rho = -0.140359 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *..* optimization finished, #iter = 217 nu = 0.054416 obj = -2.394191, rho = -0.166641 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 165 nu = 0.041789 obj = -2.589787, rho = -0.192524 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 291 nu = 0.032812 obj = -2.663932, rho = -0.190178 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 291 nu = 0.022810 obj = -2.663932, rho = -0.190178 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 291 nu = 0.015858 obj = -2.663932, rho = -0.190178 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 291 nu = 0.011024 obj = -2.663932, rho = -0.190178 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 291 nu = 0.007664 obj = -2.663932, rho = -0.190178 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 291 nu = 0.005328 obj = -2.663932, rho = -0.190178 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 39 nu = 0.573272 obj = -0.408033, rho = -0.015287 nSV = 59, nBSV = 56 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 39 nu = 0.490721 obj = -0.509302, rho = -0.013793 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 39 nu = 0.449060 obj = -0.626329, rho = -0.025961 nSV = 46, nBSV = 43 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 69 nu = 0.378006 obj = -0.754331, rho = -0.119488 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 81 nu = 0.312857 obj = -0.909570, rho = -0.189139 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 49 nu = 0.266968 obj = -1.098921, rho = -0.232045 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 52 nu = 0.224243 obj = -1.307717, rho = -0.241437 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 71 nu = 0.188291 obj = -1.546872, rho = -0.194333 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 140 nu = 0.157468 obj = -1.810299, rho = -0.157200 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.127681 obj = -2.098318, rho = -0.073678 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 260 nu = 0.101894 obj = -2.415995, rho = -0.088809 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.081776 obj = -2.794529, rho = -0.148316 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 91 nu = 0.071487 obj = -3.177793, rho = -0.170102 nSV = 10, nBSV = 4 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 170 nu = 0.057916 obj = -3.269093, rho = -0.249425 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 170 nu = 0.040263 obj = -3.269093, rho = -0.249425 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 170 nu = 0.027990 obj = -3.269093, rho = -0.249425 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 170 nu = 0.019459 obj = -3.269093, rho = -0.249425 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 170 nu = 0.013528 obj = -3.269093, rho = -0.249425 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 170 nu = 0.009404 obj = -3.269093, rho = -0.249425 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 170 nu = 0.006538 obj = -3.269093, rho = -0.249425 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 45 nu = 0.563617 obj = -0.380668, rho = -0.273345 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 46 nu = 0.475539 obj = -0.458616, rho = -0.276606 nSV = 51, nBSV = 44 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 51 nu = 0.396597 obj = -0.552278, rho = -0.238377 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 51 nu = 0.325454 obj = -0.672804, rho = -0.254199 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 20 nu = 0.279874 obj = -0.824080, rho = -0.295309 nSV = 28, nBSV = 26 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 41 nu = 0.250715 obj = -0.989928, rho = -0.240952 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 82 nu = 0.199581 obj = -1.163122, rho = -0.192792 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 94 nu = 0.163279 obj = -1.394392, rho = -0.144329 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 60 nu = 0.140938 obj = -1.647659, rho = 0.045634 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 46 nu = 0.117736 obj = -1.934771, rho = 0.082146 nSV = 14, nBSV = 9 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 198 nu = 0.097970 obj = -2.199117, rho = 0.184533 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.076891 obj = -2.433176, rho = 0.192253 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 241 nu = 0.063313 obj = -2.593357, rho = 0.334502 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*...* optimization finished, #iter = 430 nu = 0.046610 obj = -2.630766, rho = 0.202512 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*...* optimization finished, #iter = 430 nu = 0.032403 obj = -2.630766, rho = 0.202512 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*...* optimization finished, #iter = 430 nu = 0.022526 obj = -2.630766, rho = 0.202512 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*...* optimization finished, #iter = 430 nu = 0.015660 obj = -2.630766, rho = 0.202512 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*...* optimization finished, #iter = 430 nu = 0.010887 obj = -2.630766, rho = 0.202512 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*...* optimization finished, #iter = 430 nu = 0.007568 obj = -2.630766, rho = 0.202512 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*...* optimization finished, #iter = 430 nu = 0.005262 obj = -2.630766, rho = 0.202512 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 36 nu = 0.577399 obj = -0.402046, rho = -0.269095 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 43 nu = 0.502418 obj = -0.489812, rho = -0.288525 nSV = 54, nBSV = 47 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 36 nu = 0.432414 obj = -0.590149, rho = -0.243619 nSV = 45, nBSV = 41 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 43 nu = 0.361310 obj = -0.703952, rho = -0.197051 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 80 nu = 0.298707 obj = -0.830968, rho = -0.227861 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 140 nu = 0.244901 obj = -0.976858, rho = -0.261499 nSV = 29, nBSV = 19 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 60 nu = 0.200267 obj = -1.160220, rho = -0.255126 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 60 nu = 0.166324 obj = -1.377935, rho = -0.354079 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 173 nu = 0.140820 obj = -1.603227, rho = -0.410725 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.113069 obj = -1.858056, rho = -0.312937 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.092077 obj = -2.141846, rho = -0.298591 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 71 nu = 0.073260 obj = -2.460373, rho = -0.419297 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 66 nu = 0.062709 obj = -2.765604, rho = -0.535065 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) *.* optimization finished, #iter = 130 nu = 0.048506 obj = -2.946359, rho = -0.436605 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 170 nu = 0.035753 obj = -3.081511, rho = -0.350033 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 95 nu = 0.026759 obj = -3.124828, rho = -0.199506 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 95 nu = 0.018603 obj = -3.124828, rho = -0.199506 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 95 nu = 0.012932 obj = -3.124828, rho = -0.199506 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 95 nu = 0.008991 obj = -3.124828, rho = -0.199506 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 95 nu = 0.006250 obj = -3.124828, rho = -0.199506 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 36 nu = 0.542329 obj = -0.365538, rho = -0.241672 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 56 nu = 0.456726 obj = -0.438866, rho = -0.223218 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 40 nu = 0.386356 obj = -0.526173, rho = -0.203462 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 33 nu = 0.318636 obj = -0.623174, rho = -0.308583 nSV = 35, nBSV = 30 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 75 nu = 0.266084 obj = -0.734425, rho = -0.287974 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.216925 obj = -0.867552, rho = -0.239051 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 156 nu = 0.179710 obj = -1.029322, rho = -0.187873 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 142 nu = 0.150833 obj = -1.201583, rho = -0.219863 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 81 nu = 0.121669 obj = -1.390844, rho = -0.298128 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 114 nu = 0.102719 obj = -1.578892, rho = -0.380253 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.*...* optimization finished, #iter = 495 nu = 0.077801 obj = -1.763913, rho = -0.427335 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) ..* optimization finished, #iter = 286 nu = 0.059990 obj = -2.003207, rho = -0.476549 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 194 nu = 0.047513 obj = -2.295044, rho = -0.517095 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 194 nu = 0.039075 obj = -2.605993, rho = -0.582333 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 282 nu = 0.032567 obj = -2.849319, rho = -0.635022 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..* optimization finished, #iter = 252 nu = 0.024789 obj = -2.894969, rho = -0.608455 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..* optimization finished, #iter = 252 nu = 0.017233 obj = -2.894969, rho = -0.608455 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..* optimization finished, #iter = 252 nu = 0.011980 obj = -2.894969, rho = -0.608455 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..* optimization finished, #iter = 252 nu = 0.008329 obj = -2.894969, rho = -0.608455 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..* optimization finished, #iter = 252 nu = 0.005790 obj = -2.894969, rho = -0.608455 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 35 nu = 0.612798 obj = -0.428727, rho = -0.189171 nSV = 65, nBSV = 60 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 65 nu = 0.523449 obj = -0.525374, rho = -0.251489 nSV = 56, nBSV = 50 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 43 nu = 0.447406 obj = -0.647888, rho = -0.316988 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 44 nu = 0.389309 obj = -0.795563, rho = -0.386016 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 33 nu = 0.331459 obj = -0.971977, rho = -0.431387 nSV = 35, nBSV = 30 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 71 nu = 0.286429 obj = -1.177791, rho = -0.541849 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 69 nu = 0.234959 obj = -1.416246, rho = -0.579656 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 74 nu = 0.204419 obj = -1.703187, rho = -0.542499 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 32 nu = 0.171452 obj = -2.027089, rho = -0.584158 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 139 nu = 0.142697 obj = -2.364915, rho = -0.607555 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..*..* optimization finished, #iter = 431 nu = 0.112638 obj = -2.758493, rho = -0.581017 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 126 nu = 0.089777 obj = -3.300831, rho = -0.589063 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 90 nu = 0.081059 obj = -3.897862, rho = -0.484512 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 63 nu = 0.067830 obj = -4.369573, rho = -0.595496 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 87 nu = 0.055446 obj = -4.553681, rho = -0.857277 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.039014 obj = -4.555328, rho = -0.809576 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.027122 obj = -4.555328, rho = -0.809576 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.018855 obj = -4.555328, rho = -0.809576 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.013108 obj = -4.555328, rho = -0.809576 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.009113 obj = -4.555328, rho = -0.809576 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 49 nu = 0.578498 obj = -0.398956, rho = -0.097378 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 67 nu = 0.497270 obj = -0.484097, rho = -0.155721 nSV = 54, nBSV = 46 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 27 nu = 0.412990 obj = -0.588637, rho = -0.120077 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 65 nu = 0.343349 obj = -0.722569, rho = -0.173830 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 45 nu = 0.300000 obj = -0.887760, rho = -0.184033 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 47 nu = 0.252217 obj = -1.091093, rho = -0.200610 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 34 nu = 0.220194 obj = -1.333742, rho = -0.254444 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 31 nu = 0.187481 obj = -1.618183, rho = -0.290826 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 90 nu = 0.161277 obj = -1.939304, rho = -0.253138 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 71 nu = 0.137319 obj = -2.269107, rho = -0.276226 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) ..* optimization finished, #iter = 293 nu = 0.108950 obj = -2.629721, rho = -0.267108 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 171 nu = 0.086774 obj = -3.108839, rho = -0.239316 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.070598 obj = -3.727015, rho = -0.291036 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.062049 obj = -4.428380, rho = -0.573819 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 187 nu = 0.050679 obj = -5.180017, rho = -0.735989 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 189 nu = 0.043770 obj = -5.789674, rho = -0.680793 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 176 nu = 0.035023 obj = -6.175012, rho = -0.400481 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 197 nu = 0.025710 obj = -6.212664, rho = -0.282586 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 197 nu = 0.017874 obj = -6.212664, rho = -0.282586 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 197 nu = 0.012426 obj = -6.212664, rho = -0.282586 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 37 nu = 0.584212 obj = -0.377809, rho = -0.137232 nSV = 61, nBSV = 58 Total nSV = 61 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 65 nu = 0.485812 obj = -0.442069, rho = -0.156619 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 99.5% (995/1000) (classification) * optimization finished, #iter = 36 nu = 0.393368 obj = -0.512820, rho = -0.202878 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 99.3% (993/1000) (classification) * optimization finished, #iter = 37 nu = 0.317952 obj = -0.598883, rho = -0.297630 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 34 nu = 0.259406 obj = -0.698376, rho = -0.265890 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.213445 obj = -0.802603, rho = -0.138684 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 99.4% (994/1000) (classification) * optimization finished, #iter = 63 nu = 0.167464 obj = -0.919093, rho = -0.148337 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 99.4% (994/1000) (classification) * optimization finished, #iter = 43 nu = 0.133697 obj = -1.057308, rho = -0.165371 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 99.4% (994/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.105427 obj = -1.223188, rho = -0.168944 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 99.4% (994/1000) (classification) * optimization finished, #iter = 85 nu = 0.090667 obj = -1.406691, rho = -0.119817 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 99.5% (995/1000) (classification) * optimization finished, #iter = 82 nu = 0.073337 obj = -1.531851, rho = 0.068661 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .*.* optimization finished, #iter = 200 nu = 0.055498 obj = -1.612802, rho = 0.132072 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 81 nu = 0.041816 obj = -1.688613, rho = 0.102505 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 78 nu = 0.029969 obj = -1.691745, rho = 0.104250 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 78 nu = 0.020834 obj = -1.691745, rho = 0.104250 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 78 nu = 0.014484 obj = -1.691745, rho = 0.104250 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 78 nu = 0.010069 obj = -1.691745, rho = 0.104250 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 78 nu = 0.007000 obj = -1.691745, rho = 0.104250 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 78 nu = 0.004866 obj = -1.691745, rho = 0.104250 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 78 nu = 0.003383 obj = -1.691745, rho = 0.104250 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 34 nu = 0.605324 obj = -0.410105, rho = 0.100455 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 44 nu = 0.522263 obj = -0.491214, rho = 0.044501 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 47 nu = 0.429964 obj = -0.584445, rho = 0.054182 nSV = 45, nBSV = 42 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 27 nu = 0.357946 obj = -0.691526, rho = 0.068705 nSV = 37, nBSV = 34 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 55 nu = 0.298522 obj = -0.809870, rho = 0.073266 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 66 nu = 0.238817 obj = -0.945613, rho = 0.052719 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 82 nu = 0.194853 obj = -1.108321, rho = 0.051647 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 99 nu = 0.156982 obj = -1.312605, rho = -0.006676 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 288 nu = 0.129875 obj = -1.558093, rho = -0.080708 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 70 nu = 0.110530 obj = -1.842055, rho = 0.000116 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 94 nu = 0.096574 obj = -2.088997, rho = 0.194137 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 158 nu = 0.076189 obj = -2.226801, rho = 0.133491 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 148 nu = 0.057845 obj = -2.314993, rho = 0.001439 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 173 nu = 0.041070 obj = -2.317605, rho = -0.023320 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 173 nu = 0.028552 obj = -2.317605, rho = -0.023320 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 173 nu = 0.019849 obj = -2.317605, rho = -0.023320 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 173 nu = 0.013799 obj = -2.317605, rho = -0.023320 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 173 nu = 0.009593 obj = -2.317605, rho = -0.023320 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 173 nu = 0.006669 obj = -2.317605, rho = -0.023320 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 173 nu = 0.004636 obj = -2.317605, rho = -0.023320 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 34 nu = 0.601454 obj = -0.414109, rho = -0.335456 nSV = 62, nBSV = 59 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 28 nu = 0.513809 obj = -0.501823, rho = -0.306952 nSV = 53, nBSV = 50 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 61 nu = 0.427539 obj = -0.606428, rho = -0.332715 nSV = 48, nBSV = 38 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 35 nu = 0.360000 obj = -0.744175, rho = -0.331803 nSV = 38, nBSV = 34 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 42 nu = 0.315212 obj = -0.903035, rho = -0.261642 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 42 nu = 0.257949 obj = -1.093550, rho = -0.269803 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 46 nu = 0.228188 obj = -1.319142, rho = -0.191402 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 75 nu = 0.193060 obj = -1.542652, rho = -0.218925 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 160 nu = 0.158890 obj = -1.781773, rho = -0.159889 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..* optimization finished, #iter = 242 nu = 0.123819 obj = -2.061518, rho = -0.148858 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..*.* optimization finished, #iter = 365 nu = 0.099469 obj = -2.388991, rho = -0.094636 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 228 nu = 0.078360 obj = -2.810329, rho = -0.097236 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) ...*.* optimization finished, #iter = 418 nu = 0.062840 obj = -3.388699, rho = -0.041757 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) ...* optimization finished, #iter = 385 nu = 0.053631 obj = -4.161041, rho = 0.050115 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*..* optimization finished, #iter = 322 nu = 0.048834 obj = -4.990326, rho = 0.201602 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..*.* optimization finished, #iter = 338 nu = 0.039135 obj = -5.858226, rho = 0.191481 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*..* optimization finished, #iter = 446 nu = 0.032046 obj = -6.806090, rho = 0.174880 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..* optimization finished, #iter = 297 nu = 0.025486 obj = -8.042718, rho = 0.199242 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 254 nu = 0.025153 obj = -8.923833, rho = 0.473530 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 278 nu = 0.017854 obj = -8.927766, rho = 0.493102 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 34 nu = 0.578043 obj = -0.410068, rho = -0.119980 nSV = 59, nBSV = 56 Total nSV = 59 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 37 nu = 0.492470 obj = -0.509557, rho = -0.155435 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 39 nu = 0.429645 obj = -0.635483, rho = -0.170687 nSV = 45, nBSV = 41 Total nSV = 45 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 40 nu = 0.379131 obj = -0.790433, rho = -0.251557 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 43 nu = 0.317903 obj = -0.981530, rho = -0.316964 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.279432 obj = -1.219276, rho = -0.306224 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 44 nu = 0.242543 obj = -1.512441, rho = -0.441978 nSV = 26, nBSV = 21 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 22 nu = 0.215563 obj = -1.865296, rho = -0.414203 nSV = 22, nBSV = 18 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 38 nu = 0.180879 obj = -2.242347, rho = -0.402874 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 73 nu = 0.154937 obj = -2.700646, rho = -0.364963 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 89 nu = 0.128485 obj = -3.223700, rho = -0.433711 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.110285 obj = -3.832328, rho = -0.636306 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 145 nu = 0.094130 obj = -4.400283, rho = -0.376550 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ..*.* optimization finished, #iter = 311 nu = 0.071926 obj = -4.961488, rho = -0.285049 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*..* optimization finished, #iter = 451 nu = 0.058729 obj = -5.621258, rho = -0.090472 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.045475 obj = -6.286417, rho = -0.143236 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 191 nu = 0.037218 obj = -6.964337, rho = -0.280160 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) ..* optimization finished, #iter = 275 nu = 0.029492 obj = -7.128910, rho = -0.363303 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.6% (946/1000) (classification) ..* optimization finished, #iter = 275 nu = 0.020503 obj = -7.128910, rho = -0.363303 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.6% (946/1000) (classification) ..* optimization finished, #iter = 275 nu = 0.014253 obj = -7.128910, rho = -0.363303 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.6% (946/1000) (classification) * optimization finished, #iter = 50 nu = 0.651185 obj = -0.450137, rho = 0.040456 nSV = 69, nBSV = 63 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 39 nu = 0.554987 obj = -0.549983, rho = 0.032055 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 67 nu = 0.471748 obj = -0.668332, rho = 0.065344 nSV = 51, nBSV = 44 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 52 nu = 0.403173 obj = -0.811816, rho = 0.054609 nSV = 44, nBSV = 36 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 47 nu = 0.334994 obj = -0.990183, rho = 0.013783 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 73 nu = 0.282700 obj = -1.214225, rho = 0.026431 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 49 nu = 0.240382 obj = -1.489500, rho = 0.005739 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 63 nu = 0.211836 obj = -1.816562, rho = 0.080556 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 98 nu = 0.177051 obj = -2.197928, rho = 0.014448 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 72 nu = 0.153469 obj = -2.663100, rho = 0.197019 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 215 nu = 0.125289 obj = -3.172020, rho = 0.208541 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 70 nu = 0.106365 obj = -3.806120, rho = 0.071800 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 91 nu = 0.088273 obj = -4.504238, rho = 0.097493 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) .*.*....* optimization finished, #iter = 623 nu = 0.073763 obj = -5.232581, rho = 0.396416 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) ....* optimization finished, #iter = 458 nu = 0.059347 obj = -6.103914, rho = 0.630529 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) ..*.* optimization finished, #iter = 339 nu = 0.046483 obj = -7.192755, rho = 0.692530 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..*.* optimization finished, #iter = 327 nu = 0.039681 obj = -8.487553, rho = 0.863090 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 183 nu = 0.035580 obj = -9.678765, rho = 0.920874 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 226 nu = 0.028381 obj = -9.867653, rho = 0.897722 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 226 nu = 0.019730 obj = -9.867653, rho = 0.897722 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 45 nu = 0.555610 obj = -0.368516, rho = -0.037081 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 51 nu = 0.451701 obj = -0.441959, rho = -0.051196 nSV = 51, nBSV = 44 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 49 nu = 0.392210 obj = -0.532448, rho = 0.043257 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 54 nu = 0.318362 obj = -0.636396, rho = 0.044700 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 34 nu = 0.266492 obj = -0.766921, rho = -0.023839 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 43 nu = 0.230161 obj = -0.910836, rho = -0.064711 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 49 nu = 0.186665 obj = -1.071558, rho = -0.066886 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 26 nu = 0.157963 obj = -1.260688, rho = -0.064039 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 61 nu = 0.127336 obj = -1.452445, rho = -0.042031 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.107755 obj = -1.660960, rho = -0.099414 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 99 nu = 0.085557 obj = -1.817938, rho = -0.094092 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.063942 obj = -1.963765, rho = -0.091647 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..*..* optimization finished, #iter = 426 nu = 0.048317 obj = -2.125800, rho = -0.188296 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 86 nu = 0.036777 obj = -2.303711, rho = -0.279847 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 130 nu = 0.028904 obj = -2.347258, rho = -0.372788 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 130 nu = 0.020094 obj = -2.347258, rho = -0.372788 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 130 nu = 0.013969 obj = -2.347258, rho = -0.372788 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 130 nu = 0.009711 obj = -2.347258, rho = -0.372788 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 130 nu = 0.006751 obj = -2.347258, rho = -0.372788 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 130 nu = 0.004693 obj = -2.347258, rho = -0.372788 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.605016 obj = -0.435108, rho = -0.039178 nSV = 64, nBSV = 58 Total nSV = 64 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 33 nu = 0.532241 obj = -0.541491, rho = -0.034595 nSV = 54, nBSV = 52 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 31 nu = 0.458494 obj = -0.670780, rho = -0.088982 nSV = 47, nBSV = 43 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 58 nu = 0.398214 obj = -0.830209, rho = -0.081997 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 96 nu = 0.341649 obj = -1.017081, rho = -0.144723 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.288802 obj = -1.248089, rho = -0.193627 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 76 nu = 0.246377 obj = -1.536195, rho = -0.123941 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 61 nu = 0.206196 obj = -1.915031, rho = -0.077219 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 47 nu = 0.184899 obj = -2.387386, rho = -0.094609 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 80 nu = 0.162333 obj = -2.931552, rho = -0.120687 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 160 nu = 0.134416 obj = -3.578200, rho = -0.094387 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 294 nu = 0.118004 obj = -4.404678, rho = -0.215324 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 276 nu = 0.096346 obj = -5.440307, rho = -0.233108 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 260 nu = 0.086567 obj = -6.717364, rho = -0.349087 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 276 nu = 0.071557 obj = -8.192973, rho = -0.395975 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*.* optimization finished, #iter = 366 nu = 0.061739 obj = -10.182400, rho = -0.372372 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) ...*...* optimization finished, #iter = 657 nu = 0.055528 obj = -12.504246, rho = -0.393000 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) ....*.* optimization finished, #iter = 533 nu = 0.049398 obj = -14.718775, rho = -0.440545 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..........*........* optimization finished, #iter = 1843 nu = 0.040118 obj = -16.780409, rho = -0.514892 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ...........*......* optimization finished, #iter = 1750 nu = 0.035387 obj = -18.451789, rho = -0.572563 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.613394 obj = -0.429930, rho = 0.079758 nSV = 65, nBSV = 59 Total nSV = 65 Accuracy = 95% (95/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 39 nu = 0.528138 obj = -0.528582, rho = 0.068772 nSV = 56, nBSV = 50 Total nSV = 56 Accuracy = 95% (95/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 51 nu = 0.452212 obj = -0.647083, rho = 0.028595 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 95% (95/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 59 nu = 0.386484 obj = -0.789908, rho = -0.063233 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 96% (96/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 32 nu = 0.320000 obj = -0.971222, rho = -0.112728 nSV = 35, nBSV = 31 Total nSV = 35 Accuracy = 96% (96/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 97 nu = 0.276048 obj = -1.197973, rho = -0.144927 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 96% (96/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 69 nu = 0.231598 obj = -1.492694, rho = -0.114584 nSV = 26, nBSV = 22 Total nSV = 26 Accuracy = 96% (96/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 66 nu = 0.204199 obj = -1.861291, rho = -0.077796 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.174697 obj = -2.301247, rho = -0.151241 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.148428 obj = -2.893339, rho = -0.171530 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 98 nu = 0.131510 obj = -3.674438, rho = -0.230817 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*..* optimization finished, #iter = 376 nu = 0.115959 obj = -4.632299, rho = -0.191756 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 264 nu = 0.103511 obj = -5.845220, rho = -0.213750 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) ..*...* optimization finished, #iter = 581 nu = 0.097242 obj = -7.116042, rho = -0.255517 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 241 nu = 0.080586 obj = -8.300264, rho = -0.434351 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) ......* optimization finished, #iter = 654 nu = 0.069259 obj = -9.626915, rho = -0.524109 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .....*........* optimization finished, #iter = 1321 nu = 0.057388 obj = -10.410877, rho = -0.721429 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ........*.* optimization finished, #iter = 968 nu = 0.043697 obj = -10.559080, rho = -0.705594 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ........*.* optimization finished, #iter = 968 nu = 0.030378 obj = -10.559080, rho = -0.705594 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ........*.* optimization finished, #iter = 968 nu = 0.021119 obj = -10.559080, rho = -0.705594 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 83 nu = 0.561207 obj = -0.391070, rho = -0.008958 nSV = 61, nBSV = 52 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 33 nu = 0.480000 obj = -0.483036, rho = -0.070685 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 44 nu = 0.417548 obj = -0.589296, rho = -0.082125 nSV = 43, nBSV = 40 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 52 nu = 0.353718 obj = -0.710587, rho = -0.023504 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 37 nu = 0.292027 obj = -0.861134, rho = -0.052207 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 41 nu = 0.250837 obj = -1.048806, rho = -0.073257 nSV = 27, nBSV = 23 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 56 nu = 0.211704 obj = -1.269681, rho = -0.126721 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 49 nu = 0.181880 obj = -1.527419, rho = -0.085633 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 61 nu = 0.153065 obj = -1.795910, rho = 0.009087 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 67 nu = 0.126369 obj = -2.075247, rho = -0.095604 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 168 nu = 0.101483 obj = -2.398511, rho = -0.223290 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 196 nu = 0.081803 obj = -2.766243, rho = -0.165202 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 97 nu = 0.067950 obj = -3.173793, rho = -0.212579 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.057894 obj = -3.412045, rho = -0.338704 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 159 nu = 0.042534 obj = -3.452512, rho = -0.309643 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 159 nu = 0.029570 obj = -3.452512, rho = -0.309643 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 159 nu = 0.020557 obj = -3.452512, rho = -0.309643 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 159 nu = 0.014291 obj = -3.452512, rho = -0.309643 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 159 nu = 0.009935 obj = -3.452512, rho = -0.309643 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 159 nu = 0.006907 obj = -3.452512, rho = -0.309643 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 44 nu = 0.591133 obj = -0.396428, rho = -0.144244 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 41 nu = 0.496160 obj = -0.476658, rho = -0.094801 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 39 nu = 0.420844 obj = -0.571039, rho = -0.129300 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 29 nu = 0.353621 obj = -0.678035, rho = -0.088355 nSV = 38, nBSV = 34 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 36 nu = 0.293794 obj = -0.793633, rho = -0.087663 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 35 nu = 0.240759 obj = -0.913450, rho = -0.127804 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *..* optimization finished, #iter = 231 nu = 0.197554 obj = -1.037259, rho = -0.165355 nSV = 25, nBSV = 14 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 228 nu = 0.156906 obj = -1.159637, rho = -0.277985 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*...* optimization finished, #iter = 468 nu = 0.119473 obj = -1.277844, rho = -0.316961 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *..* optimization finished, #iter = 224 nu = 0.089417 obj = -1.433175, rho = -0.333024 nSV = 17, nBSV = 5 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 70 nu = 0.071646 obj = -1.634772, rho = -0.350053 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 124 nu = 0.058126 obj = -1.827050, rho = -0.277760 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 223 nu = 0.045038 obj = -1.991094, rho = -0.292242 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 180 nu = 0.036672 obj = -2.069982, rho = -0.212819 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 180 nu = 0.025494 obj = -2.069982, rho = -0.212819 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 180 nu = 0.017723 obj = -2.069982, rho = -0.212819 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 180 nu = 0.012321 obj = -2.069982, rho = -0.212819 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 180 nu = 0.008566 obj = -2.069982, rho = -0.212819 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 180 nu = 0.005955 obj = -2.069982, rho = -0.212819 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 180 nu = 0.004140 obj = -2.069982, rho = -0.212819 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 55 nu = 0.558731 obj = -0.382010, rho = -0.057282 nSV = 60, nBSV = 53 Total nSV = 60 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.464962 obj = -0.465891, rho = -0.057438 nSV = 51, nBSV = 44 Total nSV = 51 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.398514 obj = -0.571589, rho = -0.118672 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 81 nu = 0.341846 obj = -0.697964, rho = -0.062360 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 72 nu = 0.284129 obj = -0.855352, rho = -0.063018 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.241091 obj = -1.065013, rho = 0.021990 nSV = 29, nBSV = 19 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 57 nu = 0.207666 obj = -1.335693, rho = 0.104735 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 37 nu = 0.177958 obj = -1.682865, rho = 0.112198 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 26 nu = 0.163962 obj = -2.100770, rho = 0.043176 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 53 nu = 0.142433 obj = -2.563414, rho = -0.007620 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 83 nu = 0.118895 obj = -3.124952, rho = -0.057218 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 59 nu = 0.102775 obj = -3.814270, rho = -0.096041 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 73 nu = 0.086729 obj = -4.601549, rho = -0.093518 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 69 nu = 0.076359 obj = -5.522394, rho = -0.204059 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) ...*.* optimization finished, #iter = 429 nu = 0.064739 obj = -6.323539, rho = -0.210509 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) ....*...* optimization finished, #iter = 775 nu = 0.049396 obj = -7.249417, rho = -0.266047 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 249 nu = 0.038858 obj = -8.524876, rho = -0.312307 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 179 nu = 0.033903 obj = -9.998858, rho = -0.975980 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*.* optimization finished, #iter = 393 nu = 0.030994 obj = -10.775209, rho = -2.113943 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) ..*.* optimization finished, #iter = 393 nu = 0.021547 obj = -10.775209, rho = -2.113943 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 44 nu = 0.524831 obj = -0.359088, rho = -0.207957 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 37 nu = 0.445654 obj = -0.435431, rho = -0.147747 nSV = 49, nBSV = 42 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 40 nu = 0.377758 obj = -0.524602, rho = -0.081235 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.319491 obj = -0.626260, rho = -0.011140 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 80 nu = 0.263377 obj = -0.747324, rho = -0.014302 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 53 nu = 0.219532 obj = -0.895330, rho = -0.091991 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 28 nu = 0.182647 obj = -1.076545, rho = -0.112458 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 70 nu = 0.156776 obj = -1.278008, rho = -0.224347 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.125459 obj = -1.496921, rho = -0.247816 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 86 nu = 0.100967 obj = -1.785365, rho = -0.246048 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 70 nu = 0.087133 obj = -2.128044, rho = -0.474997 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 54 nu = 0.072005 obj = -2.508701, rho = -0.548347 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 38 nu = 0.060476 obj = -2.936219, rho = -0.574873 nSV = 9, nBSV = 4 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 65 nu = 0.050908 obj = -3.268433, rho = -0.734196 nSV = 8, nBSV = 2 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 64 nu = 0.041129 obj = -3.522397, rho = -0.909158 nSV = 7, nBSV = 1 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 93 nu = 0.030317 obj = -3.541089, rho = -1.005954 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 93 nu = 0.021076 obj = -3.541089, rho = -1.005954 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 93 nu = 0.014652 obj = -3.541089, rho = -1.005954 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 93 nu = 0.010186 obj = -3.541089, rho = -1.005954 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 93 nu = 0.007081 obj = -3.541089, rho = -1.005954 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 60 nu = 0.601317 obj = -0.402181, rho = -0.037022 nSV = 63, nBSV = 55 Total nSV = 63 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 35 nu = 0.501666 obj = -0.484178, rho = -0.060811 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 58 nu = 0.420129 obj = -0.582199, rho = -0.023040 nSV = 45, nBSV = 37 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 41 nu = 0.353563 obj = -0.700675, rho = -0.035951 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 26 nu = 0.300000 obj = -0.837302, rho = 0.012238 nSV = 31, nBSV = 29 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 40 nu = 0.254690 obj = -0.983188, rho = 0.160788 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 150 nu = 0.204215 obj = -1.141112, rho = 0.174154 nSV = 25, nBSV = 15 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 83 nu = 0.164110 obj = -1.325249, rho = 0.231082 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 65 nu = 0.133707 obj = -1.554265, rho = 0.242050 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 71 nu = 0.111716 obj = -1.800839, rho = 0.157757 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 83 nu = 0.090588 obj = -2.028625, rho = 0.265287 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 153 nu = 0.070782 obj = -2.269870, rho = 0.288202 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 114 nu = 0.058940 obj = -2.490094, rho = 0.278036 nSV = 10, nBSV = 4 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..* optimization finished, #iter = 291 nu = 0.044596 obj = -2.517247, rho = 0.264514 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..* optimization finished, #iter = 291 nu = 0.031003 obj = -2.517247, rho = 0.264514 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..* optimization finished, #iter = 291 nu = 0.021553 obj = -2.517247, rho = 0.264514 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..* optimization finished, #iter = 291 nu = 0.014984 obj = -2.517247, rho = 0.264514 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..* optimization finished, #iter = 291 nu = 0.010416 obj = -2.517247, rho = 0.264514 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..* optimization finished, #iter = 291 nu = 0.007241 obj = -2.517247, rho = 0.264514 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..* optimization finished, #iter = 291 nu = 0.005034 obj = -2.517247, rho = 0.264514 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 39 nu = 0.638546 obj = -0.456251, rho = -0.339755 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 95% (95/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 41 nu = 0.565720 obj = -0.563677, rho = -0.278038 nSV = 58, nBSV = 54 Total nSV = 58 Accuracy = 95% (95/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 41 nu = 0.472013 obj = -0.694473, rho = -0.317923 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 95% (95/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 51 nu = 0.409054 obj = -0.858447, rho = -0.269050 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 95% (95/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.355952 obj = -1.061296, rho = -0.328880 nSV = 38, nBSV = 33 Total nSV = 38 Accuracy = 96% (96/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 78 nu = 0.303174 obj = -1.299706, rho = -0.317388 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 96% (96/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 91 nu = 0.258316 obj = -1.597466, rho = -0.328838 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 177 nu = 0.218952 obj = -1.966436, rho = -0.310161 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 96% (96/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 74 nu = 0.181485 obj = -2.459354, rho = -0.343821 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 96% (96/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 141 nu = 0.157234 obj = -3.128538, rho = -0.480849 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 96% (96/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.137945 obj = -3.993831, rho = -0.572916 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 116 nu = 0.119619 obj = -5.177105, rho = -0.575958 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 96% (96/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 92 nu = 0.109700 obj = -6.767988, rho = -0.791616 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 96% (96/100) (classification) Accuracy = 96% (960/1000) (classification) .* optimization finished, #iter = 161 nu = 0.099991 obj = -8.768698, rho = -0.869198 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 96% (96/100) (classification) Accuracy = 94.9% (949/1000) (classification) ...*.* optimization finished, #iter = 421 nu = 0.088196 obj = -11.442792, rho = -0.918529 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 97% (97/100) (classification) Accuracy = 95.5% (955/1000) (classification) .*.* optimization finished, #iter = 259 nu = 0.077639 obj = -15.184516, rho = -0.868349 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 97% (97/100) (classification) Accuracy = 95.9% (959/1000) (classification) .* optimization finished, #iter = 144 nu = 0.074283 obj = -20.323979, rho = -1.036993 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 95.2% (952/1000) (classification) .*.* optimization finished, #iter = 165 nu = 0.073284 obj = -26.414410, rho = -1.402138 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 93.6% (936/1000) (classification) .*..* optimization finished, #iter = 319 nu = 0.071269 obj = -32.472213, rho = -1.767580 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 92.7% (927/1000) (classification) .....*....* optimization finished, #iter = 999 nu = 0.060209 obj = -37.627131, rho = -1.882741 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 92% (920/1000) (classification) * optimization finished, #iter = 57 nu = 0.552108 obj = -0.370301, rho = -0.117666 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.463026 obj = -0.447318, rho = -0.147201 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.392498 obj = -0.537627, rho = -0.149850 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 36 nu = 0.327161 obj = -0.645707, rho = -0.137947 nSV = 34, nBSV = 30 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.269414 obj = -0.772620, rho = -0.210829 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 45 nu = 0.223250 obj = -0.937480, rho = -0.241747 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 47 nu = 0.197311 obj = -1.120204, rho = -0.336576 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *..* optimization finished, #iter = 207 nu = 0.160934 obj = -1.301590, rho = -0.258469 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.129866 obj = -1.518686, rho = -0.177237 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.110571 obj = -1.764137, rho = -0.174084 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 151 nu = 0.088631 obj = -1.957574, rho = -0.109418 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 93 nu = 0.068021 obj = -2.181997, rho = -0.255486 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.051928 obj = -2.434776, rho = -0.443059 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 80 nu = 0.040718 obj = -2.727589, rho = -0.741120 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 64 nu = 0.032599 obj = -3.064410, rho = -0.838859 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.027587 obj = -3.222462, rho = -0.984085 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.019178 obj = -3.222462, rho = -0.984085 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.013333 obj = -3.222462, rho = -0.984085 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.009269 obj = -3.222462, rho = -0.984085 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.006444 obj = -3.222462, rho = -0.984085 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 41 nu = 0.558296 obj = -0.376226, rho = -0.299078 nSV = 57, nBSV = 54 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 33 nu = 0.471534 obj = -0.454166, rho = -0.305384 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 34 nu = 0.395669 obj = -0.543035, rho = -0.265481 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 58 nu = 0.329551 obj = -0.648857, rho = -0.316932 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 58 nu = 0.268246 obj = -0.784235, rho = -0.337693 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 54 nu = 0.223552 obj = -0.955002, rho = -0.388010 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.187985 obj = -1.171433, rho = -0.440076 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 60 nu = 0.165327 obj = -1.440787, rho = -0.807479 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 52 nu = 0.145937 obj = -1.728797, rho = -1.033220 nSV = 17, nBSV = 12 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 95.1% (951/1000) (classification) * optimization finished, #iter = 74 nu = 0.121158 obj = -2.012745, rho = -1.226339 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 94.3% (943/1000) (classification) .* optimization finished, #iter = 165 nu = 0.101088 obj = -2.283590, rho = -1.228041 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 95.4% (954/1000) (classification) .* optimization finished, #iter = 178 nu = 0.080707 obj = -2.575074, rho = -1.204258 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 95.7% (957/1000) (classification) .*.* optimization finished, #iter = 217 nu = 0.062368 obj = -2.861186, rho = -1.283697 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.1% (951/1000) (classification) .*.* optimization finished, #iter = 233 nu = 0.049926 obj = -3.143449, rho = -1.400114 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.3% (943/1000) (classification) .*.* optimization finished, #iter = 247 nu = 0.039609 obj = -3.216017, rho = -1.666100 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 93.3% (933/1000) (classification) .*.* optimization finished, #iter = 247 nu = 0.027536 obj = -3.216017, rho = -1.666100 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 93.3% (933/1000) (classification) .*.* optimization finished, #iter = 247 nu = 0.019143 obj = -3.216017, rho = -1.666100 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 93.3% (933/1000) (classification) .*.* optimization finished, #iter = 247 nu = 0.013308 obj = -3.216017, rho = -1.666100 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 93.3% (933/1000) (classification) .*.* optimization finished, #iter = 247 nu = 0.009252 obj = -3.216017, rho = -1.666100 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 93.3% (933/1000) (classification) .*.* optimization finished, #iter = 247 nu = 0.006432 obj = -3.216017, rho = -1.666100 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 93.3% (933/1000) (classification) * optimization finished, #iter = 41 nu = 0.527480 obj = -0.363260, rho = -0.123328 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 39 nu = 0.460000 obj = -0.440701, rho = -0.114509 nSV = 48, nBSV = 44 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 50 nu = 0.386225 obj = -0.520070, rho = -0.240432 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 62 nu = 0.314027 obj = -0.616532, rho = -0.274106 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 66 nu = 0.259847 obj = -0.734836, rho = -0.251620 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 76 nu = 0.210668 obj = -0.885077, rho = -0.264008 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.172512 obj = -1.084924, rho = -0.296913 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 90 nu = 0.150437 obj = -1.339317, rho = -0.404250 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 65 nu = 0.129263 obj = -1.652604, rho = -0.267711 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) .*..* optimization finished, #iter = 378 nu = 0.110927 obj = -2.018822, rho = -0.199926 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 99.3% (993/1000) (classification) ..*...* optimization finished, #iter = 531 nu = 0.094351 obj = -2.471014, rho = -0.235785 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 93 nu = 0.080452 obj = -2.994062, rho = -0.320629 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 148 nu = 0.069602 obj = -3.616945, rho = -0.619514 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 214 nu = 0.057177 obj = -4.337821, rho = -0.669406 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.049433 obj = -5.158936, rho = -0.872500 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 235 nu = 0.040297 obj = -6.057045, rho = -0.984240 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 190 nu = 0.031670 obj = -7.233692, rho = -1.013864 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 171 nu = 0.027071 obj = -8.847877, rho = -1.223182 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 178 nu = 0.026131 obj = -10.199730, rho = -1.900386 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 242 nu = 0.020741 obj = -10.372549, rho = -2.243582 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 44 nu = 0.609670 obj = -0.405875, rho = -0.170207 nSV = 64, nBSV = 58 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 37 nu = 0.508920 obj = -0.487345, rho = -0.125857 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 55 nu = 0.422149 obj = -0.583956, rho = -0.123477 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 35 nu = 0.356160 obj = -0.702294, rho = -0.091747 nSV = 38, nBSV = 33 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 49 nu = 0.294322 obj = -0.841072, rho = -0.169761 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 58 nu = 0.240077 obj = -1.014566, rho = -0.157198 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.208526 obj = -1.226017, rho = -0.090085 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 85 nu = 0.175116 obj = -1.455215, rho = -0.031660 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.145003 obj = -1.712928, rho = -0.050908 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*..* optimization finished, #iter = 363 nu = 0.122180 obj = -1.977056, rho = -0.070928 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.095174 obj = -2.281540, rho = -0.008798 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 146 nu = 0.077842 obj = -2.649215, rho = -0.025976 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..*.* optimization finished, #iter = 396 nu = 0.062231 obj = -3.056811, rho = -0.024937 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..* optimization finished, #iter = 236 nu = 0.050628 obj = -3.517047, rho = -0.100220 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 190 nu = 0.040731 obj = -4.041051, rho = -0.187046 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) ..* optimization finished, #iter = 274 nu = 0.035206 obj = -4.471017, rho = -0.345537 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) ..*.* optimization finished, #iter = 335 nu = 0.026950 obj = -4.527706, rho = -0.430732 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) ..*.* optimization finished, #iter = 335 nu = 0.018736 obj = -4.527706, rho = -0.430732 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) ..*.* optimization finished, #iter = 335 nu = 0.013025 obj = -4.527706, rho = -0.430732 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) ..*.* optimization finished, #iter = 335 nu = 0.009055 obj = -4.527706, rho = -0.430732 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 44 nu = 0.581658 obj = -0.396158, rho = -0.080347 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 47 nu = 0.493854 obj = -0.479823, rho = -0.030759 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 26 nu = 0.418654 obj = -0.582562, rho = -0.124796 nSV = 43, nBSV = 40 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.358164 obj = -0.698335, rho = -0.062185 nSV = 37, nBSV = 33 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 64 nu = 0.292040 obj = -0.829180, rho = -0.093733 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.239941 obj = -0.994969, rho = -0.099093 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 150 nu = 0.202491 obj = -1.196008, rho = -0.191433 nSV = 27, nBSV = 17 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.170224 obj = -1.424968, rho = -0.350672 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 86 nu = 0.140664 obj = -1.691986, rho = -0.476303 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 163 nu = 0.118963 obj = -2.002934, rho = -0.577698 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 126 nu = 0.098008 obj = -2.341848, rho = -0.523963 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 151 nu = 0.077764 obj = -2.747978, rho = -0.580973 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 167 nu = 0.063236 obj = -3.270973, rho = -0.569143 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 175 nu = 0.051527 obj = -3.886896, rho = -0.510137 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 173 nu = 0.041713 obj = -4.723509, rho = -0.485376 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 149 nu = 0.034964 obj = -5.849157, rho = -0.458013 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) ...* optimization finished, #iter = 394 nu = 0.030052 obj = -7.323186, rho = -0.457711 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .......*....* optimization finished, #iter = 1183 nu = 0.029074 obj = -8.876380, rho = -0.597076 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ........*.......* optimization finished, #iter = 1520 nu = 0.027203 obj = -9.680529, rho = -0.810686 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ....*..........* optimization finished, #iter = 1467 nu = 0.019375 obj = -9.688545, rho = -0.842633 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 75 nu = 0.554169 obj = -0.379244, rho = -0.066071 nSV = 59, nBSV = 52 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 36 nu = 0.464120 obj = -0.463218, rho = -0.084984 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.394720 obj = -0.565066, rho = -0.061587 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.331436 obj = -0.695620, rho = -0.015373 nSV = 35, nBSV = 30 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 27 nu = 0.283724 obj = -0.864057, rho = 0.023658 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 29 nu = 0.251459 obj = -1.068813, rho = -0.175891 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 57 nu = 0.211453 obj = -1.310899, rho = -0.139248 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 67 nu = 0.186238 obj = -1.594503, rho = -0.030085 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 54 nu = 0.158452 obj = -1.911963, rho = 0.062382 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 130 nu = 0.129718 obj = -2.288449, rho = 0.087475 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 150 nu = 0.104994 obj = -2.760595, rho = 0.055674 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.088979 obj = -3.414271, rho = 0.098611 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 143 nu = 0.079251 obj = -4.178481, rho = 0.041085 nSV = 11, nBSV = 5 Total nSV = 11 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 191 nu = 0.065641 obj = -5.000336, rho = -0.011803 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) ...*...* optimization finished, #iter = 620 nu = 0.055169 obj = -6.033884, rho = -0.063961 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 221 nu = 0.049882 obj = -7.178719, rho = -0.005065 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ..*.* optimization finished, #iter = 363 nu = 0.044521 obj = -7.718051, rho = 0.074478 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ...*.* optimization finished, #iter = 426 nu = 0.032024 obj = -7.738586, rho = 0.103963 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ...*.* optimization finished, #iter = 426 nu = 0.022263 obj = -7.738586, rho = 0.103963 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ...*.* optimization finished, #iter = 426 nu = 0.015477 obj = -7.738586, rho = 0.103963 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 46 nu = 0.622985 obj = -0.424093, rho = -0.096250 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 52 nu = 0.534183 obj = -0.513690, rho = -0.019155 nSV = 57, nBSV = 51 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 42 nu = 0.455863 obj = -0.611404, rho = 0.005453 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 80 nu = 0.373001 obj = -0.723118, rho = -0.041388 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.316572 obj = -0.849725, rho = 0.039749 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 83 nu = 0.258533 obj = -0.977277, rho = -0.017585 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.203154 obj = -1.117038, rho = 0.023516 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 80 nu = 0.165299 obj = -1.272183, rho = 0.003541 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 53 nu = 0.130040 obj = -1.444215, rho = -0.112094 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.105691 obj = -1.622291, rho = -0.084338 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) .*..* optimization finished, #iter = 302 nu = 0.084797 obj = -1.752615, rho = -0.074069 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) ..*.* optimization finished, #iter = 320 nu = 0.062457 obj = -1.865386, rho = -0.074791 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) ..*........* optimization finished, #iter = 1035 nu = 0.045698 obj = -1.999904, rho = -0.118243 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*..* optimization finished, #iter = 329 nu = 0.036123 obj = -2.136206, rho = -0.044191 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) ..*..* optimization finished, #iter = 417 nu = 0.026439 obj = -2.146581, rho = -0.006926 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) ..*..* optimization finished, #iter = 417 nu = 0.018380 obj = -2.146581, rho = -0.006926 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) ..*..* optimization finished, #iter = 417 nu = 0.012778 obj = -2.146581, rho = -0.006926 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) ..*..* optimization finished, #iter = 417 nu = 0.008883 obj = -2.146581, rho = -0.006926 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) ..*..* optimization finished, #iter = 417 nu = 0.006175 obj = -2.146581, rho = -0.006926 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) ..*..* optimization finished, #iter = 417 nu = 0.004293 obj = -2.146581, rho = -0.006926 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 35 nu = 0.595945 obj = -0.410896, rho = -0.101880 nSV = 61, nBSV = 58 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 33 nu = 0.513260 obj = -0.499052, rho = -0.070218 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 45 nu = 0.439279 obj = -0.600779, rho = -0.132378 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 53 nu = 0.368678 obj = -0.713715, rho = -0.246754 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 74 nu = 0.304250 obj = -0.840940, rho = -0.288697 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 58 nu = 0.252116 obj = -0.992740, rho = -0.281686 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.204213 obj = -1.150510, rho = -0.217922 nSV = 25, nBSV = 14 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 79 nu = 0.164229 obj = -1.348544, rho = -0.255475 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.133121 obj = -1.595344, rho = -0.342039 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 80 nu = 0.109808 obj = -1.887852, rho = -0.490082 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.093723 obj = -2.209766, rho = -0.771409 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.073411 obj = -2.564131, rho = -0.774701 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.057488 obj = -3.048869, rho = -0.776313 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 78 nu = 0.047439 obj = -3.723622, rho = -0.838974 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 55 nu = 0.042518 obj = -4.541298, rho = -0.967858 nSV = 8, nBSV = 2 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 81 nu = 0.039907 obj = -5.104337, rho = -1.106957 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 156 nu = 0.030989 obj = -5.206058, rho = -1.133109 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 156 nu = 0.021543 obj = -5.206058, rho = -1.133109 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 156 nu = 0.014977 obj = -5.206058, rho = -1.133109 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 156 nu = 0.010412 obj = -5.206058, rho = -1.133109 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 39 nu = 0.640000 obj = -0.435697, rho = -0.057903 nSV = 66, nBSV = 63 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 47 nu = 0.550220 obj = -0.522702, rho = -0.089974 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 51 nu = 0.465214 obj = -0.620350, rho = -0.080063 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 56 nu = 0.380000 obj = -0.729627, rho = -0.136729 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 59 nu = 0.305190 obj = -0.865515, rho = -0.120331 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 61 nu = 0.254089 obj = -1.025479, rho = 0.019247 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 58 nu = 0.211149 obj = -1.215814, rho = 0.134329 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 80 nu = 0.175044 obj = -1.424628, rho = 0.151181 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 76 nu = 0.141679 obj = -1.680846, rho = 0.063994 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 79 nu = 0.119396 obj = -1.956683, rho = 0.037785 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 89 nu = 0.093465 obj = -2.258329, rho = 0.061558 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 98 nu = 0.077634 obj = -2.642117, rho = -0.005084 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 94 nu = 0.067579 obj = -2.966406, rho = -0.007922 nSV = 10, nBSV = 4 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 129 nu = 0.053708 obj = -3.106795, rho = 0.167766 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 177 nu = 0.038345 obj = -3.113598, rho = 0.206899 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 177 nu = 0.026657 obj = -3.113598, rho = 0.206899 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 177 nu = 0.018532 obj = -3.113598, rho = 0.206899 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 177 nu = 0.012883 obj = -3.113598, rho = 0.206899 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 177 nu = 0.008956 obj = -3.113598, rho = 0.206899 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 177 nu = 0.006226 obj = -3.113598, rho = 0.206899 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 46 nu = 0.583602 obj = -0.404730, rho = -0.272512 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 49 nu = 0.510783 obj = -0.492102, rho = -0.255391 nSV = 54, nBSV = 47 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.421466 obj = -0.596307, rho = -0.284495 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 40 nu = 0.365246 obj = -0.720363, rho = -0.265488 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 44 nu = 0.310426 obj = -0.857757, rho = -0.219810 nSV = 34, nBSV = 30 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 59 nu = 0.256525 obj = -1.009444, rho = -0.264714 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *....* optimization finished, #iter = 437 nu = 0.216863 obj = -1.166226, rho = -0.144983 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.172557 obj = -1.315238, rho = -0.116718 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.132261 obj = -1.493515, rho = -0.121331 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*..* optimization finished, #iter = 344 nu = 0.105855 obj = -1.718918, rho = -0.078243 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.*.* optimization finished, #iter = 301 nu = 0.087268 obj = -1.959610, rho = -0.053251 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*...............* optimization finished, #iter = 1623 nu = 0.067732 obj = -2.185193, rho = -0.151975 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*...* optimization finished, #iter = 457 nu = 0.050941 obj = -2.476132, rho = -0.153776 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.043840 obj = -2.793916, rho = -0.236700 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 152 nu = 0.035633 obj = -2.893138, rho = -0.313233 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 152 nu = 0.024772 obj = -2.893138, rho = -0.313233 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 152 nu = 0.017221 obj = -2.893138, rho = -0.313233 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 152 nu = 0.011972 obj = -2.893138, rho = -0.313233 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 152 nu = 0.008323 obj = -2.893138, rho = -0.313233 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 152 nu = 0.005786 obj = -2.893138, rho = -0.313233 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 67 nu = 0.584530 obj = -0.411829, rho = -0.092869 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 49 nu = 0.503251 obj = -0.508733, rho = -0.072836 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 37 nu = 0.432438 obj = -0.628562, rho = -0.108276 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 29 nu = 0.384037 obj = -0.765320, rho = -0.175282 nSV = 40, nBSV = 36 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.330922 obj = -0.906652, rho = -0.155861 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 79 nu = 0.268100 obj = -1.062591, rho = -0.131962 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 69 nu = 0.222041 obj = -1.252932, rho = -0.146878 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 88 nu = 0.181154 obj = -1.461091, rho = -0.107053 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *..* optimization finished, #iter = 210 nu = 0.147138 obj = -1.707759, rho = -0.188190 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.119609 obj = -1.995697, rho = -0.241017 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..* optimization finished, #iter = 272 nu = 0.097709 obj = -2.306517, rho = -0.310099 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *..* optimization finished, #iter = 204 nu = 0.078912 obj = -2.673708, rho = -0.335849 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.069380 obj = -2.993839, rho = -0.367893 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.054490 obj = -3.076001, rho = -0.463874 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.037885 obj = -3.076001, rho = -0.463960 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.026337 obj = -3.076001, rho = -0.463960 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.018310 obj = -3.076001, rho = -0.463960 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.012729 obj = -3.076001, rho = -0.463960 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.008849 obj = -3.076001, rho = -0.463960 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.006152 obj = -3.076001, rho = -0.463960 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 45 nu = 0.629106 obj = -0.438695, rho = -0.002875 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 32 nu = 0.540000 obj = -0.540615, rho = -0.033550 nSV = 56, nBSV = 53 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 78 nu = 0.466988 obj = -0.659754, rho = 0.009879 nSV = 50, nBSV = 43 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 56 nu = 0.393704 obj = -0.802957, rho = 0.037558 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 53 nu = 0.331789 obj = -0.982126, rho = 0.071360 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 73 nu = 0.281886 obj = -1.199164, rho = 0.089905 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 121 nu = 0.236897 obj = -1.465952, rho = 0.127920 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 84 nu = 0.198737 obj = -1.819788, rho = 0.178378 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.174140 obj = -2.262636, rho = 0.090133 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.148359 obj = -2.817657, rho = 0.041034 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 195 nu = 0.129507 obj = -3.507137, rho = 0.034357 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 225 nu = 0.108018 obj = -4.421264, rho = 0.085949 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.099642 obj = -5.604605, rho = 0.243539 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 193 nu = 0.095002 obj = -6.753752, rho = 0.420315 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 212 nu = 0.078224 obj = -7.667975, rho = 0.528450 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) ..*..* optimization finished, #iter = 497 nu = 0.065627 obj = -8.451132, rho = 0.410884 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) ...*.* optimization finished, #iter = 432 nu = 0.052322 obj = -8.788724, rho = 0.331544 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ...*.* optimization finished, #iter = 432 nu = 0.036374 obj = -8.788724, rho = 0.331544 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ...*.* optimization finished, #iter = 432 nu = 0.025287 obj = -8.788724, rho = 0.331544 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ...*.* optimization finished, #iter = 432 nu = 0.017579 obj = -8.788724, rho = 0.331544 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 32 nu = 0.524083 obj = -0.341609, rho = -0.111286 nSV = 54, nBSV = 51 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 43 nu = 0.427122 obj = -0.405585, rho = -0.101413 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 59 nu = 0.353895 obj = -0.483671, rho = -0.057691 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 23 nu = 0.289651 obj = -0.580352, rho = -0.020730 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 22 nu = 0.252053 obj = -0.693367, rho = -0.025930 nSV = 27, nBSV = 22 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 38 nu = 0.200159 obj = -0.822848, rho = 0.002867 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 79 nu = 0.169414 obj = -0.984693, rho = 0.086659 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 136 nu = 0.140852 obj = -1.165601, rho = 0.009671 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 79 nu = 0.115318 obj = -1.383544, rho = -0.035319 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 40 nu = 0.099141 obj = -1.633160, rho = 0.025376 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 54 nu = 0.078124 obj = -1.894939, rho = 0.067368 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 44 nu = 0.066768 obj = -2.211549, rho = 0.046142 nSV = 11, nBSV = 5 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 43 nu = 0.053996 obj = -2.474121, rho = 0.161290 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 30 nu = 0.044049 obj = -2.711533, rho = 0.073595 nSV = 7, nBSV = 2 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 74 nu = 0.033997 obj = -2.760099, rho = -0.104177 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 74 nu = 0.023635 obj = -2.760099, rho = -0.104177 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 74 nu = 0.016431 obj = -2.760099, rho = -0.104177 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 74 nu = 0.011422 obj = -2.760099, rho = -0.104177 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 74 nu = 0.007941 obj = -2.760099, rho = -0.104177 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 74 nu = 0.005520 obj = -2.760099, rho = -0.104177 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 49 nu = 0.631973 obj = -0.449430, rho = -0.215572 nSV = 66, nBSV = 61 Total nSV = 66 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 52 nu = 0.546930 obj = -0.555796, rho = -0.155049 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 36 nu = 0.475543 obj = -0.683839, rho = -0.099122 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 51 nu = 0.405785 obj = -0.836660, rho = -0.054046 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 97% (97/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 64 nu = 0.341935 obj = -1.023462, rho = -0.024364 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 75 nu = 0.288238 obj = -1.262837, rho = -0.021406 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 70 nu = 0.252864 obj = -1.552487, rho = -0.079588 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 77 nu = 0.223943 obj = -1.886324, rho = -0.002520 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 289 nu = 0.185085 obj = -2.252426, rho = -0.004946 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) ...* optimization finished, #iter = 373 nu = 0.152223 obj = -2.704321, rho = -0.016201 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 200 nu = 0.122620 obj = -3.306495, rho = -0.013806 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 136 nu = 0.104663 obj = -4.116935, rho = -0.069332 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) ...* optimization finished, #iter = 382 nu = 0.087717 obj = -5.215903, rho = -0.056776 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..* optimization finished, #iter = 258 nu = 0.077621 obj = -6.686009, rho = -0.202394 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 205 nu = 0.070607 obj = -8.560678, rho = -0.294577 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) ...* optimization finished, #iter = 389 nu = 0.063892 obj = -10.739785, rho = -0.416786 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) .....*...* optimization finished, #iter = 873 nu = 0.054650 obj = -13.471599, rho = -0.601640 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 95.6% (956/1000) (classification) .*.............*.* optimization finished, #iter = 1503 nu = 0.045900 obj = -17.218674, rho = -0.647436 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 95.4% (954/1000) (classification) ..........*..* optimization finished, #iter = 1229 nu = 0.041464 obj = -22.186653, rho = -0.891580 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 95.2% (952/1000) (classification) ..* optimization finished, #iter = 244 nu = 0.038121 obj = -28.709163, rho = -1.267003 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 41 nu = 0.600000 obj = -0.415560, rho = -0.182027 nSV = 62, nBSV = 59 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 56 nu = 0.531546 obj = -0.501426, rho = -0.142887 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 71 nu = 0.438656 obj = -0.594631, rho = -0.064442 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 99 nu = 0.360889 obj = -0.703416, rho = -0.004859 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 74 nu = 0.297446 obj = -0.836604, rho = -0.008505 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 87 nu = 0.244549 obj = -0.995059, rho = 0.001935 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.201984 obj = -1.186634, rho = 0.040445 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.171201 obj = -1.418584, rho = 0.153537 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 70 nu = 0.140959 obj = -1.678351, rho = 0.125893 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 71 nu = 0.118721 obj = -1.969861, rho = 0.169000 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 192 nu = 0.096268 obj = -2.265637, rho = 0.066130 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.077317 obj = -2.611724, rho = 0.146828 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..*.* optimization finished, #iter = 300 nu = 0.064051 obj = -2.964978, rho = 0.126946 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 179 nu = 0.052645 obj = -3.256215, rho = 0.113028 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 179 nu = 0.041592 obj = -3.377070, rho = 0.090938 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 179 nu = 0.028915 obj = -3.377070, rho = 0.090938 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 179 nu = 0.020101 obj = -3.377070, rho = 0.090938 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 179 nu = 0.013974 obj = -3.377070, rho = 0.090938 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 179 nu = 0.009715 obj = -3.377070, rho = 0.090938 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 179 nu = 0.006754 obj = -3.377070, rho = 0.090938 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 39 nu = 0.569994 obj = -0.380552, rho = 0.057095 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 63 nu = 0.473910 obj = -0.457486, rho = 0.070029 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 38 nu = 0.393051 obj = -0.550749, rho = 0.099188 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 27 nu = 0.339932 obj = -0.661555, rho = 0.224186 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 45 nu = 0.279031 obj = -0.785386, rho = 0.227333 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.229159 obj = -0.934526, rho = 0.197059 nSV = 26, nBSV = 21 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.194984 obj = -1.109143, rho = 0.126664 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *..* optimization finished, #iter = 273 nu = 0.160160 obj = -1.290884, rho = 0.142485 nSV = 21, nBSV = 10 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 97 nu = 0.131110 obj = -1.505877, rho = 0.117019 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.111783 obj = -1.715949, rho = 0.018529 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*..* optimization finished, #iter = 332 nu = 0.086030 obj = -1.869623, rho = -0.048606 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.068342 obj = -2.021771, rho = -0.067306 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.049702 obj = -2.141974, rho = -0.085752 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) ..* optimization finished, #iter = 242 nu = 0.038178 obj = -2.257408, rho = -0.147212 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.028310 obj = -2.298547, rho = -0.125119 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.019681 obj = -2.298547, rho = -0.125119 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.013682 obj = -2.298547, rho = -0.125119 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.009512 obj = -2.298547, rho = -0.125119 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.006612 obj = -2.298547, rho = -0.125119 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.004597 obj = -2.298547, rho = -0.125119 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 36 nu = 0.613539 obj = -0.431123, rho = -0.036399 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.532570 obj = -0.529054, rho = -0.036647 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 52 nu = 0.460284 obj = -0.638577, rho = -0.022850 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 68 nu = 0.386352 obj = -0.768095, rho = -0.051868 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 85 nu = 0.325558 obj = -0.914221, rho = -0.063307 nSV = 37, nBSV = 28 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 38 nu = 0.264216 obj = -1.094384, rho = -0.081811 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 35 nu = 0.228695 obj = -1.291700, rho = -0.120817 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 70 nu = 0.187362 obj = -1.509992, rho = -0.059239 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *..* optimization finished, #iter = 224 nu = 0.156316 obj = -1.749957, rho = -0.035387 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) ..*..* optimization finished, #iter = 485 nu = 0.122469 obj = -1.998443, rho = -0.138436 nSV = 18, nBSV = 7 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 92 nu = 0.100710 obj = -2.292578, rho = -0.143112 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 86 nu = 0.086148 obj = -2.506796, rho = -0.291758 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) ..* optimization finished, #iter = 224 nu = 0.063360 obj = -2.591176, rho = -0.305963 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 171 nu = 0.046119 obj = -2.659093, rho = -0.362587 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 170 nu = 0.032773 obj = -2.661096, rho = -0.332223 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 170 nu = 0.022784 obj = -2.661096, rho = -0.332223 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 170 nu = 0.015839 obj = -2.661096, rho = -0.332223 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 170 nu = 0.011011 obj = -2.661096, rho = -0.332223 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 170 nu = 0.007655 obj = -2.661096, rho = -0.332223 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 170 nu = 0.005322 obj = -2.661096, rho = -0.332223 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 57 nu = 0.606520 obj = -0.418767, rho = -0.156417 nSV = 64, nBSV = 57 Total nSV = 64 Accuracy = 96% (96/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 35 nu = 0.525258 obj = -0.511351, rho = -0.147891 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 77 nu = 0.442606 obj = -0.617339, rho = -0.217417 nSV = 50, nBSV = 41 Total nSV = 50 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 63 nu = 0.370118 obj = -0.746498, rho = -0.229413 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 65 nu = 0.310188 obj = -0.901108, rho = -0.257026 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) *..* optimization finished, #iter = 231 nu = 0.258918 obj = -1.090511, rho = -0.279820 nSV = 31, nBSV = 21 Total nSV = 31 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 86 nu = 0.220200 obj = -1.333663, rho = -0.348980 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.181118 obj = -1.632195, rho = -0.343678 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 94 nu = 0.155759 obj = -2.017913, rho = -0.385405 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 73 nu = 0.136767 obj = -2.488400, rho = -0.351444 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.117041 obj = -3.027745, rho = -0.230607 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 70 nu = 0.097864 obj = -3.704288, rho = -0.006927 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.082146 obj = -4.556239, rho = 0.034387 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 58 nu = 0.073919 obj = -5.609204, rho = 0.215809 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.065983 obj = -6.579685, rho = 0.216010 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 207 nu = 0.055319 obj = -7.336535, rho = -0.031479 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 165 nu = 0.043312 obj = -8.054972, rho = 0.029220 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 172 nu = 0.033801 obj = -8.169855, rho = 0.073014 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 172 nu = 0.023498 obj = -8.169855, rho = 0.073014 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 172 nu = 0.016336 obj = -8.169855, rho = 0.073014 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 38 nu = 0.577589 obj = -0.378037, rho = -0.078436 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 45 nu = 0.485965 obj = -0.445078, rho = -0.103421 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.406685 obj = -0.515330, rho = -0.130670 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 71 nu = 0.323020 obj = -0.587681, rho = -0.099377 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.259498 obj = -0.669719, rho = -0.062944 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 51 nu = 0.209221 obj = -0.750234, rho = -0.057552 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 84 nu = 0.164658 obj = -0.818521, rho = -0.066967 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 85 nu = 0.124051 obj = -0.887873, rho = -0.152711 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 165 nu = 0.096128 obj = -0.944813, rho = -0.146287 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..* optimization finished, #iter = 260 nu = 0.069762 obj = -0.997937, rho = -0.171607 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 190 nu = 0.050549 obj = -1.064389, rho = -0.163496 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *...* optimization finished, #iter = 314 nu = 0.037959 obj = -1.142514, rho = -0.203179 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.029662 obj = -1.208560, rho = -0.418481 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.021473 obj = -1.212055, rho = -0.481487 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.014928 obj = -1.212055, rho = -0.481487 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.010378 obj = -1.212055, rho = -0.481487 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.007215 obj = -1.212055, rho = -0.481487 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.005016 obj = -1.212055, rho = -0.481487 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.003487 obj = -1.212055, rho = -0.481487 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.002424 obj = -1.212055, rho = -0.481487 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 53 nu = 0.620000 obj = -0.414843, rho = -0.064482 nSV = 63, nBSV = 60 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 35 nu = 0.520000 obj = -0.499812, rho = -0.036224 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 61 nu = 0.430294 obj = -0.598841, rho = -0.020308 nSV = 47, nBSV = 40 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 48 nu = 0.355193 obj = -0.725205, rho = -0.000986 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 34 nu = 0.302027 obj = -0.883633, rho = 0.029077 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 82 nu = 0.257845 obj = -1.064369, rho = -0.086710 nSV = 31, nBSV = 21 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 52 nu = 0.218112 obj = -1.272486, rho = -0.154330 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 95 nu = 0.178040 obj = -1.523587, rho = -0.217275 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 39 nu = 0.152510 obj = -1.819182, rho = -0.386655 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) ....*...* optimization finished, #iter = 747 nu = 0.125756 obj = -2.147094, rho = -0.524689 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.105326 obj = -2.516526, rho = -0.626848 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 220 nu = 0.087547 obj = -2.918008, rho = -0.586270 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 196 nu = 0.070905 obj = -3.324410, rho = -0.592314 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 146 nu = 0.055040 obj = -3.733241, rho = -0.647654 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .*.* optimization finished, #iter = 234 nu = 0.043590 obj = -4.162655, rho = -0.676269 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .* optimization finished, #iter = 193 nu = 0.037339 obj = -4.399978, rho = -0.914130 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) ..* optimization finished, #iter = 288 nu = 0.026250 obj = -4.410145, rho = -0.926616 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) ..* optimization finished, #iter = 288 nu = 0.018249 obj = -4.410145, rho = -0.926616 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) ..* optimization finished, #iter = 288 nu = 0.012687 obj = -4.410145, rho = -0.926616 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) ..* optimization finished, #iter = 288 nu = 0.008820 obj = -4.410145, rho = -0.926616 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 37 nu = 0.548782 obj = -0.385221, rho = -0.277984 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 36 nu = 0.468894 obj = -0.476004, rho = -0.230311 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 52 nu = 0.413665 obj = -0.581617, rho = -0.148136 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.360000 obj = -0.700588, rho = -0.242327 nSV = 37, nBSV = 33 Total nSV = 37 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) *...* optimization finished, #iter = 304 nu = 0.295074 obj = -0.831876, rho = -0.247121 nSV = 34, nBSV = 24 Total nSV = 34 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.237563 obj = -1.004522, rho = -0.250596 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 94 nu = 0.200886 obj = -1.223420, rho = -0.320784 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 52 nu = 0.169349 obj = -1.491790, rho = -0.325064 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.140859 obj = -1.841564, rho = -0.313303 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 86 nu = 0.124891 obj = -2.287485, rho = -0.352067 nSV = 15, nBSV = 10 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 156 nu = 0.109991 obj = -2.759879, rho = -0.414890 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*..* optimization finished, #iter = 332 nu = 0.091073 obj = -3.268815, rho = -0.468065 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*....* optimization finished, #iter = 530 nu = 0.078143 obj = -3.857641, rho = -0.501731 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) ....*.* optimization finished, #iter = 558 nu = 0.066127 obj = -4.364634, rho = -0.491036 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .*.* optimization finished, #iter = 260 nu = 0.051141 obj = -4.860900, rho = -0.480826 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) ...*.* optimization finished, #iter = 403 nu = 0.041073 obj = -5.284176, rho = -0.437119 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..* optimization finished, #iter = 256 nu = 0.032688 obj = -5.491919, rho = -0.396707 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) ..* optimization finished, #iter = 256 nu = 0.022724 obj = -5.491919, rho = -0.396707 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) ..* optimization finished, #iter = 256 nu = 0.015798 obj = -5.491919, rho = -0.396707 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) ..* optimization finished, #iter = 256 nu = 0.010983 obj = -5.491919, rho = -0.396707 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 55 nu = 0.550669 obj = -0.373031, rho = -0.078639 nSV = 58, nBSV = 51 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 33 nu = 0.460812 obj = -0.454055, rho = 0.012261 nSV = 48, nBSV = 44 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 79 nu = 0.390621 obj = -0.551611, rho = -0.067051 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 30 nu = 0.328548 obj = -0.674061, rho = -0.004920 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 35 nu = 0.284595 obj = -0.818110, rho = 0.000112 nSV = 31, nBSV = 27 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 32 nu = 0.241010 obj = -0.979249, rho = -0.070484 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 81 nu = 0.199346 obj = -1.165100, rho = -0.083356 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 146 nu = 0.166545 obj = -1.386787, rho = -0.079664 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 87 nu = 0.135683 obj = -1.654847, rho = -0.085558 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) .*...* optimization finished, #iter = 415 nu = 0.112544 obj = -1.978541, rho = -0.034569 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 125 nu = 0.095608 obj = -2.380637, rho = -0.112717 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) .*.* optimization finished, #iter = 266 nu = 0.078543 obj = -2.831885, rho = -0.096797 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) ...* optimization finished, #iter = 321 nu = 0.064391 obj = -3.368576, rho = -0.103738 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.1% (961/1000) (classification) .*...* optimization finished, #iter = 403 nu = 0.051418 obj = -4.100913, rho = -0.113152 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 96% (960/1000) (classification) ..* optimization finished, #iter = 266 nu = 0.043697 obj = -5.112028, rho = -0.180407 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 95.8% (958/1000) (classification) .* optimization finished, #iter = 137 nu = 0.041900 obj = -6.195224, rho = -0.610312 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 88 nu = 0.037004 obj = -7.001452, rho = -0.873471 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 94.7% (947/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.029545 obj = -7.141204, rho = -1.127664 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 94.4% (944/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.020540 obj = -7.141204, rho = -1.127664 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 94.4% (944/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.014279 obj = -7.141204, rho = -1.127664 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 94.4% (944/1000) (classification) * optimization finished, #iter = 40 nu = 0.626723 obj = -0.416496, rho = -0.167249 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 34 nu = 0.520502 obj = -0.499179, rho = -0.183629 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 54 nu = 0.438868 obj = -0.591345, rho = -0.140053 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 75 nu = 0.360105 obj = -0.699169, rho = -0.121682 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 54 nu = 0.293952 obj = -0.835320, rho = -0.085175 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 154 nu = 0.242208 obj = -1.002543, rho = -0.137840 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 53 nu = 0.207402 obj = -1.202877, rho = -0.130526 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.172338 obj = -1.417294, rho = -0.116700 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.139140 obj = -1.679816, rho = -0.084481 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.113110 obj = -2.003880, rho = -0.055656 nSV = 18, nBSV = 7 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.093254 obj = -2.443148, rho = -0.010947 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 77 nu = 0.082809 obj = -2.966656, rho = -0.139076 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.070901 obj = -3.499866, rho = -0.195028 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 265 nu = 0.056026 obj = -4.062726, rho = -0.225036 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.045312 obj = -4.812438, rho = -0.343040 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) ...*.................* optimization finished, #iter = 2049 nu = 0.037644 obj = -5.710134, rho = -0.555282 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 223 nu = 0.032892 obj = -6.669215, rho = -0.835956 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.029221 obj = -7.062593, rho = -1.116250 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.020314 obj = -7.062593, rho = -1.116250 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.014122 obj = -7.062593, rho = -1.116250 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 34 nu = 0.583683 obj = -0.387642, rho = -0.250995 nSV = 60, nBSV = 57 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 55 nu = 0.482142 obj = -0.464267, rho = -0.283726 nSV = 53, nBSV = 45 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 35 nu = 0.403556 obj = -0.558889, rho = -0.284002 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 44 nu = 0.340000 obj = -0.668575, rho = -0.252754 nSV = 36, nBSV = 32 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 65 nu = 0.284031 obj = -0.787900, rho = -0.201191 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 96 nu = 0.238059 obj = -0.923449, rho = -0.132122 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.190065 obj = -1.080338, rho = -0.128566 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.153692 obj = -1.269459, rho = -0.149126 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 230 nu = 0.124411 obj = -1.510817, rho = -0.136292 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 63 nu = 0.105982 obj = -1.793201, rho = -0.182785 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 230 nu = 0.085710 obj = -2.088217, rho = -0.202617 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 178 nu = 0.067209 obj = -2.487721, rho = -0.201177 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) .* optimization finished, #iter = 177 nu = 0.054947 obj = -3.050464, rho = -0.138278 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.050038 obj = -3.748358, rho = 0.020893 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.041780 obj = -4.395020, rho = 0.115470 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.033427 obj = -5.258174, rho = 0.137824 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 99% (99/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 82 nu = 0.028716 obj = -6.327182, rho = 0.214633 nSV = 7, nBSV = 1 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.026243 obj = -7.269817, rho = 0.231339 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 94.5% (945/1000) (classification) .* optimization finished, #iter = 181 nu = 0.021477 obj = -7.467619, rho = 0.216438 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94% (940/1000) (classification) .* optimization finished, #iter = 181 nu = 0.014930 obj = -7.467619, rho = 0.216438 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94% (940/1000) (classification) * optimization finished, #iter = 42 nu = 0.540761 obj = -0.370969, rho = -0.186916 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 35 nu = 0.475098 obj = -0.447243, rho = -0.184531 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.394181 obj = -0.531984, rho = -0.170048 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 32 nu = 0.328676 obj = -0.628989, rho = -0.084916 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 68 nu = 0.263320 obj = -0.743899, rho = -0.128661 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 34 nu = 0.220941 obj = -0.879744, rho = -0.221495 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 55 nu = 0.182630 obj = -1.037890, rho = -0.302779 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 94 nu = 0.151483 obj = -1.200014, rho = -0.344862 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 65 nu = 0.122827 obj = -1.377913, rho = -0.260478 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 113 nu = 0.099610 obj = -1.558475, rho = -0.213205 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) ...* optimization finished, #iter = 359 nu = 0.076803 obj = -1.740921, rho = -0.197749 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) .....*.* optimization finished, #iter = 692 nu = 0.060282 obj = -1.958681, rho = -0.109280 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) ...*..* optimization finished, #iter = 559 nu = 0.047066 obj = -2.213607, rho = -0.030379 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*..* optimization finished, #iter = 321 nu = 0.036365 obj = -2.509897, rho = -0.028627 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 64 nu = 0.031567 obj = -2.802063, rho = 0.093679 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 78 nu = 0.024284 obj = -2.836377, rho = 0.183387 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 78 nu = 0.016882 obj = -2.836377, rho = 0.183387 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 78 nu = 0.011736 obj = -2.836377, rho = 0.183387 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 78 nu = 0.008159 obj = -2.836377, rho = 0.183387 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 78 nu = 0.005672 obj = -2.836377, rho = 0.183387 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 38 nu = 0.632683 obj = -0.455437, rho = -0.222760 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 78 nu = 0.551465 obj = -0.566334, rho = -0.152548 nSV = 59, nBSV = 52 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 34 nu = 0.483628 obj = -0.699838, rho = -0.177206 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 76 nu = 0.411207 obj = -0.861635, rho = -0.122798 nSV = 46, nBSV = 38 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.354735 obj = -1.062610, rho = -0.032681 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 59 nu = 0.303614 obj = -1.305040, rho = -0.024360 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 47 nu = 0.260000 obj = -1.615232, rho = -0.091892 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 42 nu = 0.219779 obj = -2.002825, rho = -0.130084 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.192296 obj = -2.485973, rho = -0.159316 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.163467 obj = -3.107975, rho = -0.261258 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 170 nu = 0.153471 obj = -3.778903, rho = -0.550073 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) ......*.* optimization finished, #iter = 718 nu = 0.122517 obj = -4.487674, rho = -0.546570 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) ..* optimization finished, #iter = 299 nu = 0.101834 obj = -5.464075, rho = -0.603333 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) ..*..............* optimization finished, #iter = 1654 nu = 0.084683 obj = -6.649993, rho = -0.748120 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .....*..................* optimization finished, #iter = 2374 nu = 0.073159 obj = -8.118793, rho = -0.782929 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 239 nu = 0.060513 obj = -10.000898, rho = -0.726810 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 155 nu = 0.052834 obj = -12.337204, rho = -0.722613 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 244 nu = 0.048974 obj = -14.711111, rho = -1.005594 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) .*.* optimization finished, #iter = 264 nu = 0.039212 obj = -17.022146, rho = -0.774956 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*.* optimization finished, #iter = 315 nu = 0.032473 obj = -19.497329, rho = -0.625275 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 72 nu = 0.626084 obj = -0.438042, rho = -0.336419 nSV = 66, nBSV = 59 Total nSV = 66 Accuracy = 96% (96/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 42 nu = 0.537259 obj = -0.540218, rho = -0.326493 nSV = 56, nBSV = 50 Total nSV = 56 Accuracy = 96% (96/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 77 nu = 0.453410 obj = -0.665449, rho = -0.307008 nSV = 49, nBSV = 42 Total nSV = 49 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.385638 obj = -0.828585, rho = -0.356658 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 43 nu = 0.336570 obj = -1.036565, rho = -0.395579 nSV = 36, nBSV = 32 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 49 nu = 0.297128 obj = -1.294038, rho = -0.304720 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.251019 obj = -1.601233, rho = -0.364898 nSV = 30, nBSV = 21 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.213349 obj = -2.018766, rho = -0.386314 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.195384 obj = -2.546095, rho = -0.284675 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 64 nu = 0.164469 obj = -3.174227, rho = -0.254022 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 55 nu = 0.145111 obj = -4.023961, rho = -0.180342 nSV = 18, nBSV = 13 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 199 nu = 0.134667 obj = -4.961623, rho = -0.350623 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..* optimization finished, #iter = 278 nu = 0.112573 obj = -5.962803, rho = -0.445529 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) ...*....* optimization finished, #iter = 759 nu = 0.100002 obj = -7.023250, rho = -0.410523 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .......*.* optimization finished, #iter = 894 nu = 0.079958 obj = -8.035627, rho = -0.247998 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) ...*..* optimization finished, #iter = 577 nu = 0.062946 obj = -9.368491, rho = -0.300818 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) ...*.* optimization finished, #iter = 440 nu = 0.053741 obj = -10.788305, rho = -0.493645 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ...............*.* optimization finished, #iter = 1606 nu = 0.044435 obj = -11.753695, rho = -0.478742 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ...................* optimization finished, #iter = 1979 nu = 0.032241 obj = -12.617811, rho = -0.448132 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ................*.......* optimization finished, #iter = 2318 nu = 0.026309 obj = -13.155620, rho = -0.405874 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 41 nu = 0.579462 obj = -0.392937, rho = -0.109979 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 38 nu = 0.478695 obj = -0.478068, rho = -0.140163 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 72 nu = 0.403864 obj = -0.586854, rho = -0.087923 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 40 nu = 0.344031 obj = -0.727375, rho = -0.193363 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 58 nu = 0.300161 obj = -0.893837, rho = -0.147969 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 49 nu = 0.257271 obj = -1.095959, rho = -0.248108 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 94 nu = 0.217763 obj = -1.347292, rho = -0.205816 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 83 nu = 0.190701 obj = -1.641477, rho = -0.036750 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.164022 obj = -1.968001, rho = -0.090531 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 276 nu = 0.134808 obj = -2.334401, rho = -0.103589 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 96 nu = 0.110330 obj = -2.765724, rho = -0.216648 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*..* optimization finished, #iter = 351 nu = 0.088600 obj = -3.329431, rho = -0.172723 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 88 nu = 0.077387 obj = -4.067678, rho = -0.151019 nSV = 11, nBSV = 6 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 173 nu = 0.068585 obj = -4.707245, rho = -0.090864 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 216 nu = 0.053279 obj = -5.385852, rho = -0.493143 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 212 nu = 0.041929 obj = -6.269153, rho = -0.721770 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 142 nu = 0.037216 obj = -7.278239, rho = -1.136549 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 242 nu = 0.031079 obj = -7.511594, rho = -1.556324 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 242 nu = 0.021606 obj = -7.511594, rho = -1.556324 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 242 nu = 0.015020 obj = -7.511594, rho = -1.556324 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 45 nu = 0.632383 obj = -0.419967, rho = 0.068433 nSV = 67, nBSV = 61 Total nSV = 67 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 55 nu = 0.534143 obj = -0.497390, rho = 0.018367 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 73 nu = 0.441383 obj = -0.581260, rho = 0.052367 nSV = 50, nBSV = 41 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 73 nu = 0.363472 obj = -0.676915, rho = 0.024907 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.291160 obj = -0.784573, rho = 0.122249 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 53 nu = 0.231576 obj = -0.915596, rho = 0.204664 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *..* optimization finished, #iter = 203 nu = 0.183139 obj = -1.081755, rho = 0.235320 nSV = 27, nBSV = 15 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 79 nu = 0.151335 obj = -1.303016, rho = 0.180994 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 55 nu = 0.124270 obj = -1.595438, rho = 0.220809 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 73 nu = 0.110026 obj = -1.951616, rho = 0.580180 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 54 nu = 0.099722 obj = -2.263883, rho = 0.836316 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 59 nu = 0.081510 obj = -2.505440, rho = 1.084516 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) ..*.* optimization finished, #iter = 315 nu = 0.063640 obj = -2.645970, rho = 1.115858 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ...* optimization finished, #iter = 381 nu = 0.047519 obj = -2.728125, rho = 1.040931 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..* optimization finished, #iter = 291 nu = 0.033632 obj = -2.731057, rho = 1.026477 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..* optimization finished, #iter = 291 nu = 0.023381 obj = -2.731057, rho = 1.026477 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..* optimization finished, #iter = 291 nu = 0.016254 obj = -2.731057, rho = 1.026477 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..* optimization finished, #iter = 291 nu = 0.011300 obj = -2.731057, rho = 1.026477 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..* optimization finished, #iter = 291 nu = 0.007856 obj = -2.731057, rho = 1.026477 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..* optimization finished, #iter = 291 nu = 0.005461 obj = -2.731057, rho = 1.026477 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 39 nu = 0.615690 obj = -0.421076, rho = -0.129992 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 46 nu = 0.530649 obj = -0.507562, rho = -0.040618 nSV = 56, nBSV = 50 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 34 nu = 0.448212 obj = -0.604941, rho = -0.097888 nSV = 47, nBSV = 43 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 66 nu = 0.371703 obj = -0.711750, rho = -0.157947 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 84 nu = 0.300339 obj = -0.838570, rho = -0.155334 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 48 nu = 0.248930 obj = -0.992501, rho = -0.071526 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 91 nu = 0.213848 obj = -1.162657, rho = -0.180977 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 143 nu = 0.170655 obj = -1.332074, rho = -0.153419 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*....* optimization finished, #iter = 553 nu = 0.139980 obj = -1.504797, rho = -0.053683 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) ..*.* optimization finished, #iter = 318 nu = 0.113160 obj = -1.651959, rho = 0.061040 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) ..*.* optimization finished, #iter = 321 nu = 0.086915 obj = -1.744615, rho = 0.086216 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) ..* optimization finished, #iter = 298 nu = 0.063994 obj = -1.784449, rho = 0.075942 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .....*..* optimization finished, #iter = 730 nu = 0.045447 obj = -1.794065, rho = 0.048741 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) ........*....* optimization finished, #iter = 1281 nu = 0.031832 obj = -1.797055, rho = 0.047946 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) ........*....* optimization finished, #iter = 1281 nu = 0.022130 obj = -1.797055, rho = 0.047946 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) ........*....* optimization finished, #iter = 1281 nu = 0.015384 obj = -1.797055, rho = 0.047946 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) ........*....* optimization finished, #iter = 1281 nu = 0.010695 obj = -1.797055, rho = 0.047946 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) ........*....* optimization finished, #iter = 1281 nu = 0.007435 obj = -1.797055, rho = 0.047946 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) ........*....* optimization finished, #iter = 1281 nu = 0.005169 obj = -1.797055, rho = 0.047946 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) ........*....* optimization finished, #iter = 1281 nu = 0.003593 obj = -1.797055, rho = 0.047946 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 34 nu = 0.590739 obj = -0.415806, rho = -0.315601 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 36 nu = 0.512577 obj = -0.510097, rho = -0.334349 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 44 nu = 0.439406 obj = -0.623682, rho = -0.258921 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.376084 obj = -0.755974, rho = -0.240311 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 46 nu = 0.318339 obj = -0.913902, rho = -0.222344 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 66 nu = 0.263704 obj = -1.103314, rho = -0.194921 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 38 nu = 0.229030 obj = -1.320120, rho = -0.142656 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 54 nu = 0.190436 obj = -1.549611, rho = -0.291364 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.157560 obj = -1.801478, rho = -0.286865 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 241 nu = 0.124897 obj = -2.090898, rho = -0.307760 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 205 nu = 0.103972 obj = -2.395098, rho = -0.316683 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 166 nu = 0.083386 obj = -2.735454, rho = -0.290869 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 176 nu = 0.066443 obj = -3.052328, rho = -0.201908 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 92 nu = 0.052457 obj = -3.403110, rho = -0.096304 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 90 nu = 0.043218 obj = -3.608395, rho = 0.101703 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.030949 obj = -3.615085, rho = 0.137744 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.021516 obj = -3.615085, rho = 0.137744 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.014958 obj = -3.615085, rho = 0.137744 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.010398 obj = -3.615085, rho = 0.137744 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.007229 obj = -3.615085, rho = 0.137744 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 37 nu = 0.607754 obj = -0.396654, rho = -0.184897 nSV = 62, nBSV = 59 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 61 nu = 0.498163 obj = -0.467968, rho = -0.214615 nSV = 55, nBSV = 48 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 44 nu = 0.414899 obj = -0.550329, rho = -0.192976 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 41 nu = 0.345966 obj = -0.645105, rho = -0.240493 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 78 nu = 0.280299 obj = -0.746674, rho = -0.259007 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.226464 obj = -0.858689, rho = -0.297959 nSV = 28, nBSV = 18 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.180553 obj = -0.987436, rho = -0.258998 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 71 nu = 0.145266 obj = -1.132959, rho = -0.355497 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 169 nu = 0.117789 obj = -1.275108, rho = -0.465811 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 131 nu = 0.093728 obj = -1.426929, rho = -0.420603 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 192 nu = 0.071620 obj = -1.558668, rho = -0.426303 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..*.* optimization finished, #iter = 382 nu = 0.055961 obj = -1.713396, rho = -0.381732 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 283 nu = 0.042112 obj = -1.842308, rho = -0.373634 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ...*.* optimization finished, #iter = 480 nu = 0.032741 obj = -1.974138, rho = -0.328896 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ...*..* optimization finished, #iter = 535 nu = 0.024522 obj = -1.991348, rho = -0.312215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ...*..* optimization finished, #iter = 535 nu = 0.017048 obj = -1.991348, rho = -0.312215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ...*..* optimization finished, #iter = 535 nu = 0.011852 obj = -1.991348, rho = -0.312215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ...*..* optimization finished, #iter = 535 nu = 0.008239 obj = -1.991348, rho = -0.312215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ...*..* optimization finished, #iter = 535 nu = 0.005728 obj = -1.991348, rho = -0.312215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ...*..* optimization finished, #iter = 535 nu = 0.003982 obj = -1.991348, rho = -0.312215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 56 nu = 0.577403 obj = -0.400362, rho = -0.161668 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 63 nu = 0.490139 obj = -0.487868, rho = -0.108003 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 32 nu = 0.418835 obj = -0.598223, rho = -0.113306 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 40 nu = 0.352316 obj = -0.733068, rho = -0.129722 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 96% (96/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 34 nu = 0.302200 obj = -0.903311, rho = -0.170067 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 51 nu = 0.252714 obj = -1.111994, rho = -0.194329 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.219120 obj = -1.381426, rho = -0.211089 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 53 nu = 0.185649 obj = -1.716781, rho = -0.218258 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 89 nu = 0.161309 obj = -2.146696, rho = -0.337200 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 164 nu = 0.137910 obj = -2.711546, rho = -0.279108 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 228 nu = 0.119626 obj = -3.461499, rho = -0.206616 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 91 nu = 0.111021 obj = -4.427040, rho = -0.306186 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 87 nu = 0.106344 obj = -5.412955, rho = -0.306773 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.089236 obj = -6.236288, rho = -0.308561 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.070893 obj = -7.199684, rho = -0.431611 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..* optimization finished, #iter = 252 nu = 0.061132 obj = -8.122260, rho = -0.665200 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ...* optimization finished, #iter = 382 nu = 0.050458 obj = -8.478904, rho = -0.903175 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) ...* optimization finished, #iter = 382 nu = 0.035078 obj = -8.478904, rho = -0.903175 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) ...* optimization finished, #iter = 382 nu = 0.024386 obj = -8.478904, rho = -0.903175 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) ...* optimization finished, #iter = 382 nu = 0.016953 obj = -8.478904, rho = -0.903175 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 61 nu = 0.596038 obj = -0.391857, rho = -0.229818 nSV = 64, nBSV = 56 Total nSV = 64 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 56 nu = 0.496324 obj = -0.466774, rho = -0.254766 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 41 nu = 0.414899 obj = -0.554853, rho = -0.247339 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 50 nu = 0.342874 obj = -0.655205, rho = -0.178092 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 91 nu = 0.284282 obj = -0.759134, rho = -0.131330 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 63 nu = 0.226540 obj = -0.881272, rho = -0.104460 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 50 nu = 0.184249 obj = -1.028930, rho = -0.078520 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 59 nu = 0.150361 obj = -1.186004, rho = -0.099879 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 94 nu = 0.123847 obj = -1.350789, rho = -0.135975 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .**.* optimization finished, #iter = 198 nu = 0.096403 obj = -1.506211, rho = -0.186095 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *..*.* optimization finished, #iter = 275 nu = 0.073643 obj = -1.693431, rho = -0.206409 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 83 nu = 0.060455 obj = -1.914484, rho = -0.170034 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 46 nu = 0.049084 obj = -2.058099, rho = -0.167863 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 64 nu = 0.036951 obj = -2.085818, rho = -0.252013 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 64 nu = 0.025688 obj = -2.085818, rho = -0.252013 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 64 nu = 0.017858 obj = -2.085818, rho = -0.252013 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 64 nu = 0.012415 obj = -2.085818, rho = -0.252013 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 64 nu = 0.008631 obj = -2.085818, rho = -0.252013 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 64 nu = 0.006000 obj = -2.085818, rho = -0.252013 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 64 nu = 0.004171 obj = -2.085818, rho = -0.252013 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 0.565268 obj = -0.385535, rho = -0.203249 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 38 nu = 0.480744 obj = -0.467333, rho = -0.131727 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 65 nu = 0.414122 obj = -0.556800, rho = -0.067158 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 47 nu = 0.345144 obj = -0.657576, rho = -0.036639 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 64 nu = 0.280685 obj = -0.769834, rho = 0.013033 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.230829 obj = -0.902407, rho = 0.026758 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.189947 obj = -1.049155, rho = 0.083043 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 75 nu = 0.154839 obj = -1.212102, rho = 0.150609 nSV = 18, nBSV = 13 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.125415 obj = -1.365627, rho = 0.245594 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.099611 obj = -1.508361, rho = 0.222370 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 96 nu = 0.079439 obj = -1.634992, rho = 0.210087 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 168 nu = 0.058977 obj = -1.724544, rho = 0.204198 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.043027 obj = -1.806053, rho = 0.183845 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.032924 obj = -1.858303, rho = 0.161519 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.022889 obj = -1.858303, rho = 0.161519 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.015912 obj = -1.858303, rho = 0.161519 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.011062 obj = -1.858303, rho = 0.161519 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.007690 obj = -1.858303, rho = 0.161519 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.005346 obj = -1.858303, rho = 0.161519 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.003717 obj = -1.858303, rho = 0.161519 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 53 nu = 0.560855 obj = -0.373226, rho = 0.159690 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 97% (97/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 38 nu = 0.469529 obj = -0.447769, rho = 0.144605 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 80 nu = 0.395096 obj = -0.533538, rho = 0.085575 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 83 nu = 0.328662 obj = -0.625978, rho = 0.114141 nSV = 38, nBSV = 30 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 54 nu = 0.271674 obj = -0.735984, rho = 0.157475 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 38 nu = 0.220410 obj = -0.857501, rho = 0.175123 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 49 nu = 0.181466 obj = -0.980402, rho = 0.151550 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.143615 obj = -1.115529, rho = 0.122236 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 64 nu = 0.115148 obj = -1.275985, rho = 0.226692 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 52 nu = 0.093818 obj = -1.425247, rho = 0.243107 nSV = 11, nBSV = 5 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.075394 obj = -1.520531, rho = 0.063933 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*.*.* optimization finished, #iter = 394 nu = 0.055312 obj = -1.571035, rho = 0.038807 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 175 nu = 0.039462 obj = -1.616338, rho = 0.035894 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 133 nu = 0.029301 obj = -1.654118, rho = 0.087684 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 133 nu = 0.020370 obj = -1.654118, rho = 0.087684 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 133 nu = 0.014161 obj = -1.654118, rho = 0.087684 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 133 nu = 0.009845 obj = -1.654118, rho = 0.087684 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 133 nu = 0.006844 obj = -1.654118, rho = 0.087684 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 133 nu = 0.004758 obj = -1.654118, rho = 0.087684 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 133 nu = 0.003308 obj = -1.654118, rho = 0.087684 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 46 nu = 0.612608 obj = -0.401921, rho = -0.224178 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 48 nu = 0.513757 obj = -0.478024, rho = -0.256156 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 58 nu = 0.419634 obj = -0.564868, rho = -0.260755 nSV = 46, nBSV = 38 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 63 nu = 0.343910 obj = -0.668912, rho = -0.221315 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 51 nu = 0.291251 obj = -0.791572, rho = -0.241141 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 89 nu = 0.235281 obj = -0.928808, rho = -0.289051 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.190038 obj = -1.092449, rho = -0.383331 nSV = 25, nBSV = 15 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 82 nu = 0.156130 obj = -1.288838, rho = -0.394049 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 84 nu = 0.124799 obj = -1.531648, rho = -0.368174 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*..* optimization finished, #iter = 327 nu = 0.102236 obj = -1.853614, rho = -0.448948 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *....* optimization finished, #iter = 409 nu = 0.087672 obj = -2.257229, rho = -0.520627 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*.* optimization finished, #iter = 311 nu = 0.073334 obj = -2.729308, rho = -0.577430 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 264 nu = 0.059749 obj = -3.361097, rho = -0.626482 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 37 nu = 0.054600 obj = -4.146958, rho = -0.954026 nSV = 8, nBSV = 3 Total nSV = 8 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 84 nu = 0.048103 obj = -4.839089, rho = -1.023320 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 67 nu = 0.039182 obj = -5.525345, rho = -0.691343 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 139 nu = 0.030549 obj = -6.289153, rho = -0.511238 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 158 nu = 0.025779 obj = -7.082021, rho = -0.115826 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 156 nu = 0.020979 obj = -7.295184, rho = 0.264824 nSV = 6, nBSV = 0 Total nSV = 6 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 156 nu = 0.014584 obj = -7.295184, rho = 0.264824 nSV = 6, nBSV = 0 Total nSV = 6 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 41 nu = 0.586583 obj = -0.393749, rho = -0.200905 nSV = 60, nBSV = 57 Total nSV = 60 Accuracy = 97% (97/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 41 nu = 0.492223 obj = -0.474291, rho = -0.225022 nSV = 52, nBSV = 45 Total nSV = 52 Accuracy = 97% (97/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 55 nu = 0.407441 obj = -0.571925, rho = -0.272416 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 31 nu = 0.345727 obj = -0.691585, rho = -0.251835 nSV = 37, nBSV = 33 Total nSV = 37 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 26 nu = 0.289965 obj = -0.825880, rho = -0.225905 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 51 nu = 0.243574 obj = -0.982790, rho = -0.266793 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.200317 obj = -1.167311, rho = -0.269084 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 150 nu = 0.161007 obj = -1.403016, rho = -0.253838 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 82 nu = 0.132615 obj = -1.725290, rho = -0.270791 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 52 nu = 0.110641 obj = -2.159449, rho = -0.243033 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 88 nu = 0.102434 obj = -2.692070, rho = -0.299494 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.087073 obj = -3.263095, rho = -0.260812 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.071111 obj = -4.033335, rho = -0.295400 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 152 nu = 0.062856 obj = -5.057935, rho = -0.396318 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) ...*...* optimization finished, #iter = 649 nu = 0.057891 obj = -6.180408, rho = -0.463044 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*..* optimization finished, #iter = 487 nu = 0.051459 obj = -7.081821, rho = -0.463713 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 149 nu = 0.043245 obj = -7.635701, rho = -0.390252 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 180 nu = 0.031753 obj = -7.675235, rho = -0.320409 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .* optimization finished, #iter = 180 nu = 0.022075 obj = -7.675235, rho = -0.320409 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .* optimization finished, #iter = 180 nu = 0.015346 obj = -7.675235, rho = -0.320409 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 47 nu = 0.644608 obj = -0.451308, rho = -0.037252 nSV = 66, nBSV = 64 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 37 nu = 0.554377 obj = -0.554180, rho = -0.038565 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.479290 obj = -0.676789, rho = -0.042747 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 54 nu = 0.408293 obj = -0.814613, rho = -0.079185 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.335864 obj = -0.984019, rho = -0.056561 nSV = 39, nBSV = 30 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.287694 obj = -1.199420, rho = 0.021683 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 141 nu = 0.240845 obj = -1.454129, rho = -0.062343 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 130 nu = 0.201282 obj = -1.778917, rho = -0.164379 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 67 nu = 0.172840 obj = -2.167183, rho = -0.193552 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 141 nu = 0.146033 obj = -2.621814, rho = -0.301532 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 158 nu = 0.118320 obj = -3.230480, rho = -0.327070 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 59 nu = 0.101742 obj = -4.056413, rho = -0.295380 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) .*.* optimization finished, #iter = 224 nu = 0.091492 obj = -5.069704, rho = -0.286112 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 155 nu = 0.081221 obj = -6.209270, rho = 0.006882 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) ..* optimization finished, #iter = 279 nu = 0.070610 obj = -7.357522, rho = 0.445210 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .....*.* optimization finished, #iter = 640 nu = 0.058554 obj = -8.544499, rho = 0.745439 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 269 nu = 0.048267 obj = -9.851568, rho = 0.908158 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 192 nu = 0.043606 obj = -10.539573, rho = 1.316338 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 192 nu = 0.030315 obj = -10.539573, rho = 1.316338 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 192 nu = 0.021075 obj = -10.539573, rho = 1.316338 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 46 nu = 0.605650 obj = -0.410470, rho = -0.155758 nSV = 64, nBSV = 58 Total nSV = 64 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.516477 obj = -0.495884, rho = -0.167838 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 80 nu = 0.434888 obj = -0.595957, rho = -0.007350 nSV = 47, nBSV = 40 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 30 nu = 0.369931 obj = -0.708571, rho = 0.074240 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 33 nu = 0.301834 obj = -0.832190, rho = 0.039894 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 39 nu = 0.255010 obj = -0.968472, rho = -0.029060 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 44 nu = 0.200587 obj = -1.112478, rho = -0.015841 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 74 nu = 0.166111 obj = -1.278030, rho = 0.023783 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 284 nu = 0.129849 obj = -1.446404, rho = 0.012250 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 231 nu = 0.101448 obj = -1.663338, rho = -0.003630 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 186 nu = 0.084051 obj = -1.901573, rho = -0.145795 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 184 nu = 0.066717 obj = -2.134396, rho = -0.235671 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 245 nu = 0.050835 obj = -2.381281, rho = -0.254416 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ...*.........* optimization finished, #iter = 1221 nu = 0.042633 obj = -2.628282, rho = -0.162490 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..*......* optimization finished, #iter = 852 nu = 0.032554 obj = -2.738228, rho = -0.182743 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*....* optimization finished, #iter = 527 nu = 0.023529 obj = -2.748114, rho = -0.171141 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*....* optimization finished, #iter = 527 nu = 0.016357 obj = -2.748114, rho = -0.171141 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*....* optimization finished, #iter = 527 nu = 0.011371 obj = -2.748114, rho = -0.171141 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*....* optimization finished, #iter = 527 nu = 0.007905 obj = -2.748114, rho = -0.171141 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*....* optimization finished, #iter = 527 nu = 0.005496 obj = -2.748114, rho = -0.171141 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 44 nu = 0.643855 obj = -0.434094, rho = -0.022972 nSV = 66, nBSV = 61 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 56 nu = 0.543219 obj = -0.524121, rho = -0.022116 nSV = 58, nBSV = 51 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 42 nu = 0.458142 obj = -0.625880, rho = 0.068569 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 40 nu = 0.382131 obj = -0.743937, rho = 0.116471 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 91 nu = 0.313274 obj = -0.881572, rho = 0.083721 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 69 nu = 0.261019 obj = -1.050635, rho = 0.106039 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 78 nu = 0.214961 obj = -1.241354, rho = 0.132705 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 62 nu = 0.178383 obj = -1.466844, rho = 0.142496 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.144351 obj = -1.726902, rho = 0.140011 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 59 nu = 0.120387 obj = -2.048827, rho = 0.132878 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.104589 obj = -2.352167, rho = 0.123966 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 201 nu = 0.080596 obj = -2.630042, rho = 0.152420 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ....*..* optimization finished, #iter = 659 nu = 0.061024 obj = -3.007857, rho = 0.149064 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 166 nu = 0.051577 obj = -3.461753, rho = 0.114181 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 95 nu = 0.043763 obj = -3.725596, rho = 0.127351 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 165 nu = 0.032112 obj = -3.750282, rho = 0.327355 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 165 nu = 0.022324 obj = -3.750282, rho = 0.327355 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 165 nu = 0.015519 obj = -3.750282, rho = 0.327355 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 165 nu = 0.010789 obj = -3.750282, rho = 0.327355 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 165 nu = 0.007500 obj = -3.750282, rho = 0.327355 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 41 nu = 0.563398 obj = -0.369652, rho = 0.053465 nSV = 58, nBSV = 54 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 37 nu = 0.476650 obj = -0.438191, rho = 0.038974 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 57 nu = 0.387815 obj = -0.510799, rho = -0.015749 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 54 nu = 0.321755 obj = -0.593637, rho = -0.133297 nSV = 35, nBSV = 30 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 89 nu = 0.260634 obj = -0.679445, rho = -0.149995 nSV = 31, nBSV = 21 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 67 nu = 0.206484 obj = -0.778658, rho = -0.161552 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 52 nu = 0.164916 obj = -0.887451, rho = -0.250862 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 163 nu = 0.134418 obj = -0.977775, rho = -0.401321 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 71 nu = 0.105793 obj = -1.064796, rho = -0.324264 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 97 nu = 0.080793 obj = -1.116290, rho = -0.316053 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.059006 obj = -1.144998, rho = -0.326766 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 221 nu = 0.042090 obj = -1.148072, rho = -0.386409 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 221 nu = 0.029260 obj = -1.148072, rho = -0.386409 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 221 nu = 0.020342 obj = -1.148072, rho = -0.386409 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 221 nu = 0.014141 obj = -1.148072, rho = -0.386409 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 221 nu = 0.009831 obj = -1.148072, rho = -0.386409 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 221 nu = 0.006834 obj = -1.148072, rho = -0.386409 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 221 nu = 0.004751 obj = -1.148072, rho = -0.386409 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 221 nu = 0.003303 obj = -1.148072, rho = -0.386409 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 221 nu = 0.002296 obj = -1.148072, rho = -0.386409 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.603910 obj = -0.419525, rho = -0.386432 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 36 nu = 0.520492 obj = -0.512282, rho = -0.396068 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 31 nu = 0.441897 obj = -0.622695, rho = -0.343189 nSV = 46, nBSV = 43 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 37 nu = 0.373335 obj = -0.752307, rho = -0.473081 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 37 nu = 0.316430 obj = -0.912208, rho = -0.435457 nSV = 33, nBSV = 29 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 41 nu = 0.265356 obj = -1.100452, rho = -0.451777 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 42 nu = 0.237013 obj = -1.293674, rho = -0.350622 nSV = 26, nBSV = 21 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 73 nu = 0.190133 obj = -1.487338, rho = -0.381925 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 195 nu = 0.150077 obj = -1.691398, rho = -0.418043 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.119042 obj = -1.946252, rho = -0.344471 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) .*..* optimization finished, #iter = 377 nu = 0.094146 obj = -2.262542, rho = -0.359523 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 290 nu = 0.076512 obj = -2.660088, rho = -0.378360 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) ...*.* optimization finished, #iter = 462 nu = 0.061253 obj = -3.129604, rho = -0.515506 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) ...*..* optimization finished, #iter = 534 nu = 0.052944 obj = -3.678061, rho = -0.678768 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*.......* optimization finished, #iter = 943 nu = 0.042153 obj = -4.160750, rho = -0.739046 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.......* optimization finished, #iter = 771 nu = 0.032445 obj = -4.786709, rho = -0.685620 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 132 nu = 0.027868 obj = -5.545570, rho = -0.558248 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 152 nu = 0.023906 obj = -5.778103, rho = -0.423491 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 152 nu = 0.016620 obj = -5.778103, rho = -0.423491 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 152 nu = 0.011554 obj = -5.778103, rho = -0.423491 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 45 nu = 0.646832 obj = -0.443278, rho = -0.108912 nSV = 68, nBSV = 63 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 48 nu = 0.540796 obj = -0.540512, rho = -0.145198 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 42 nu = 0.458878 obj = -0.664560, rho = -0.130408 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 61 nu = 0.387574 obj = -0.820720, rho = -0.140923 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 82 nu = 0.332719 obj = -1.014672, rho = -0.232588 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 51 nu = 0.287363 obj = -1.260086, rho = -0.201520 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 59 nu = 0.246909 obj = -1.564834, rho = -0.179192 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 50 nu = 0.217659 obj = -1.928910, rho = -0.244204 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 96 nu = 0.186698 obj = -2.370502, rho = -0.233003 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 94 nu = 0.157572 obj = -2.902238, rho = -0.262623 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 94 nu = 0.134012 obj = -3.575255, rho = -0.327585 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 88 nu = 0.116629 obj = -4.381623, rho = -0.469808 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 292 nu = 0.096979 obj = -5.363013, rho = -0.470038 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*..* optimization finished, #iter = 416 nu = 0.084637 obj = -6.638811, rho = -0.239937 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 271 nu = 0.076385 obj = -7.968704, rho = 0.177659 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .....*...................* optimization finished, #iter = 2434 nu = 0.062945 obj = -9.181134, rho = 0.419267 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..*.* optimization finished, #iter = 390 nu = 0.050331 obj = -10.721253, rho = 0.409308 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..*.* optimization finished, #iter = 344 nu = 0.044026 obj = -12.186816, rho = 0.408069 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ...*.* optimization finished, #iter = 414 nu = 0.036289 obj = -12.613378, rho = 0.405956 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) ...*.* optimization finished, #iter = 414 nu = 0.025228 obj = -12.613378, rho = 0.405956 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 46 nu = 0.590839 obj = -0.400057, rho = -0.332545 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.504841 obj = -0.481718, rho = -0.281146 nSV = 54, nBSV = 47 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.417195 obj = -0.578848, rho = -0.304618 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.355066 obj = -0.698035, rho = -0.283769 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 39 nu = 0.297609 obj = -0.831255, rho = -0.269977 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 74 nu = 0.246086 obj = -0.977400, rho = -0.302474 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 93 nu = 0.201548 obj = -1.153726, rho = -0.319030 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 45 nu = 0.172207 obj = -1.341398, rho = -0.319098 nSV = 20, nBSV = 15 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 92 nu = 0.141320 obj = -1.503747, rho = -0.300806 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 182 nu = 0.109964 obj = -1.650639, rho = -0.458653 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 173 nu = 0.082821 obj = -1.805685, rho = -0.506299 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) ...*.* optimization finished, #iter = 428 nu = 0.063116 obj = -1.990370, rho = -0.508642 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) ....*..* optimization finished, #iter = 650 nu = 0.047342 obj = -2.216454, rho = -0.512598 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) ..*...........* optimization finished, #iter = 1386 nu = 0.035833 obj = -2.522638, rho = -0.486421 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 134 nu = 0.029653 obj = -2.924689, rho = -0.346210 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 133 nu = 0.026681 obj = -3.116622, rho = -0.091379 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 133 nu = 0.018548 obj = -3.116622, rho = -0.091379 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 133 nu = 0.012895 obj = -3.116622, rho = -0.091379 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 133 nu = 0.008964 obj = -3.116622, rho = -0.091379 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 133 nu = 0.006232 obj = -3.116622, rho = -0.091379 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 46 nu = 0.584105 obj = -0.386639, rho = -0.190437 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 42 nu = 0.482142 obj = -0.462598, rho = -0.223665 nSV = 51, nBSV = 44 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 76 nu = 0.396695 obj = -0.554721, rho = -0.228671 nSV = 44, nBSV = 35 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 57 nu = 0.344030 obj = -0.665181, rho = -0.166336 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 47 nu = 0.283296 obj = -0.785806, rho = -0.128754 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.235033 obj = -0.926266, rho = -0.220778 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 235 nu = 0.192365 obj = -1.076257, rho = -0.219294 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 37 nu = 0.154192 obj = -1.265288, rho = -0.202481 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *....* optimization finished, #iter = 424 nu = 0.129095 obj = -1.471181, rho = -0.129402 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 183 nu = 0.102574 obj = -1.699453, rho = -0.084254 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.085600 obj = -1.952309, rho = -0.077731 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 97 nu = 0.068646 obj = -2.177002, rho = -0.096325 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 92 nu = 0.056888 obj = -2.351255, rho = -0.323760 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .* optimization finished, #iter = 159 nu = 0.042019 obj = -2.371474, rho = -0.389893 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .* optimization finished, #iter = 159 nu = 0.029211 obj = -2.371474, rho = -0.389893 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .* optimization finished, #iter = 159 nu = 0.020307 obj = -2.371474, rho = -0.389893 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .* optimization finished, #iter = 159 nu = 0.014118 obj = -2.371474, rho = -0.389893 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .* optimization finished, #iter = 159 nu = 0.009814 obj = -2.371474, rho = -0.389893 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .* optimization finished, #iter = 159 nu = 0.006823 obj = -2.371474, rho = -0.389893 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .* optimization finished, #iter = 159 nu = 0.004743 obj = -2.371474, rho = -0.389893 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 52 nu = 0.612250 obj = -0.423831, rho = -0.098375 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 41 nu = 0.520280 obj = -0.518850, rho = -0.111750 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 72 nu = 0.444464 obj = -0.635030, rho = -0.085547 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 39 nu = 0.378247 obj = -0.776363, rho = -0.017670 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.323431 obj = -0.947297, rho = 0.004896 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 56 nu = 0.270963 obj = -1.157245, rho = 0.018346 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 26 nu = 0.232784 obj = -1.414343, rho = -0.001274 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 49 nu = 0.197041 obj = -1.710396, rho = -0.004574 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 71 nu = 0.165658 obj = -2.085581, rho = -0.025979 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *..* optimization finished, #iter = 251 nu = 0.136626 obj = -2.547783, rho = -0.029947 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 225 nu = 0.118126 obj = -3.144254, rho = -0.023505 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.101049 obj = -3.885220, rho = 0.118555 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 97 nu = 0.092508 obj = -4.733789, rho = 0.352433 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 264 nu = 0.079542 obj = -5.429590, rho = 0.535287 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 187 nu = 0.063380 obj = -6.210435, rho = 0.703446 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..*.* optimization finished, #iter = 394 nu = 0.056552 obj = -6.655605, rho = 1.004870 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ...*..* optimization finished, #iter = 501 nu = 0.039625 obj = -6.656724, rho = 1.015336 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ...*..* optimization finished, #iter = 501 nu = 0.027547 obj = -6.656724, rho = 1.015336 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ...*..* optimization finished, #iter = 501 nu = 0.019151 obj = -6.656724, rho = 1.015336 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ...*..* optimization finished, #iter = 501 nu = 0.013313 obj = -6.656724, rho = 1.015336 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 35 nu = 0.536131 obj = -0.357587, rho = -0.170726 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 77 nu = 0.449542 obj = -0.427816, rho = -0.130097 nSV = 50, nBSV = 43 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 34 nu = 0.382655 obj = -0.506384, rho = -0.121674 nSV = 40, nBSV = 36 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 55 nu = 0.310759 obj = -0.591427, rho = -0.202116 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 65 nu = 0.255432 obj = -0.688285, rho = -0.210718 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *..* optimization finished, #iter = 209 nu = 0.207094 obj = -0.793962, rho = -0.108008 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 51 nu = 0.170176 obj = -0.912739, rho = -0.088785 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 76 nu = 0.138436 obj = -1.021717, rho = -0.148859 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 245 nu = 0.105362 obj = -1.123264, rho = -0.125575 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *..* optimization finished, #iter = 246 nu = 0.080653 obj = -1.251039, rho = -0.100311 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.062287 obj = -1.402373, rho = -0.069253 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.048020 obj = -1.577687, rho = -0.108055 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*..* optimization finished, #iter = 335 nu = 0.038911 obj = -1.764640, rho = -0.070978 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 80 nu = 0.029954 obj = -1.946809, rho = -0.105205 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.025212 obj = -2.047505, rho = -0.073040 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.017527 obj = -2.047505, rho = -0.073040 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.012185 obj = -2.047505, rho = -0.073040 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.008471 obj = -2.047505, rho = -0.073040 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.005889 obj = -2.047505, rho = -0.073040 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.004094 obj = -2.047505, rho = -0.073040 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 63 nu = 0.594408 obj = -0.409913, rho = -0.148663 nSV = 61, nBSV = 54 Total nSV = 61 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 27 nu = 0.500981 obj = -0.504018, rho = -0.153539 nSV = 52, nBSV = 50 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 28 nu = 0.436905 obj = -0.611662, rho = -0.176457 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 63 nu = 0.370611 obj = -0.740906, rho = -0.198669 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 25 nu = 0.311290 obj = -0.891108, rho = -0.157564 nSV = 34, nBSV = 30 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 59 nu = 0.260958 obj = -1.059480, rho = -0.157378 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.215709 obj = -1.259907, rho = -0.161892 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 97 nu = 0.175317 obj = -1.520070, rho = -0.180598 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 47 nu = 0.154815 obj = -1.829438, rho = -0.197720 nSV = 17, nBSV = 12 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.133179 obj = -2.085751, rho = -0.231737 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) .*.* optimization finished, #iter = 280 nu = 0.101826 obj = -2.352786, rho = -0.264854 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..*.* optimization finished, #iter = 322 nu = 0.080782 obj = -2.682597, rho = -0.276651 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) ...*...* optimization finished, #iter = 628 nu = 0.063832 obj = -3.058929, rho = -0.297021 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) .*...* optimization finished, #iter = 433 nu = 0.048686 obj = -3.544441, rho = -0.292908 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 195 nu = 0.039646 obj = -4.219386, rho = -0.362386 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) .*.* optimization finished, #iter = 237 nu = 0.033227 obj = -4.947377, rho = -0.513323 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 96 nu = 0.028006 obj = -5.819128, rho = -0.594687 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.025814 obj = -6.239222, rho = -0.940040 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.017946 obj = -6.239222, rho = -0.940040 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.012476 obj = -6.239222, rho = -0.940040 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 50 nu = 0.586620 obj = -0.407038, rho = -0.133815 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 31 nu = 0.495289 obj = -0.501327, rho = -0.147535 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 36 nu = 0.432480 obj = -0.616736, rho = -0.141187 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 32 nu = 0.376465 obj = -0.745386, rho = -0.113832 nSV = 38, nBSV = 34 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 52 nu = 0.312667 obj = -0.891162, rho = -0.073513 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 59 nu = 0.265241 obj = -1.059218, rho = -0.046830 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 58 nu = 0.217892 obj = -1.252374, rho = -0.093336 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 46 nu = 0.193172 obj = -1.445400, rho = -0.181697 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.149691 obj = -1.604518, rho = -0.231660 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 88 nu = 0.117531 obj = -1.774322, rho = -0.185857 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 70 nu = 0.090744 obj = -1.948375, rho = -0.193379 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 276 nu = 0.073057 obj = -2.073713, rho = -0.077876 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) ...*.* optimization finished, #iter = 425 nu = 0.053077 obj = -2.129363, rho = -0.103949 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) ..*.* optimization finished, #iter = 376 nu = 0.037831 obj = -2.135091, rho = -0.100184 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) ..*.* optimization finished, #iter = 376 nu = 0.026300 obj = -2.135091, rho = -0.100184 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) ..*.* optimization finished, #iter = 376 nu = 0.018283 obj = -2.135091, rho = -0.100184 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) ..*.* optimization finished, #iter = 376 nu = 0.012711 obj = -2.135091, rho = -0.100184 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) ..*.* optimization finished, #iter = 376 nu = 0.008836 obj = -2.135091, rho = -0.100184 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) ..*.* optimization finished, #iter = 376 nu = 0.006143 obj = -2.135091, rho = -0.100184 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) ..*.* optimization finished, #iter = 376 nu = 0.004271 obj = -2.135091, rho = -0.100184 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 49 nu = 0.622594 obj = -0.415862, rho = -0.210118 nSV = 66, nBSV = 59 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 36 nu = 0.514416 obj = -0.502656, rho = -0.240831 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 39 nu = 0.440735 obj = -0.600952, rho = -0.224402 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 67 nu = 0.364236 obj = -0.716671, rho = -0.225931 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 39 nu = 0.300781 obj = -0.860656, rho = -0.175976 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 46 nu = 0.248441 obj = -1.038457, rho = -0.145281 nSV = 28, nBSV = 24 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 73 nu = 0.207962 obj = -1.252990, rho = -0.133478 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 84 nu = 0.172711 obj = -1.525572, rho = -0.269688 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 93 nu = 0.141429 obj = -1.891222, rho = -0.253209 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 71 nu = 0.125742 obj = -2.377259, rho = -0.160379 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.106522 obj = -2.977574, rho = -0.241012 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.093609 obj = -3.778263, rho = -0.174863 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 84 nu = 0.081063 obj = -4.795602, rho = -0.082656 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 80 nu = 0.072488 obj = -6.104904, rho = -0.016612 nSV = 10, nBSV = 4 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 85 nu = 0.065147 obj = -7.732487, rho = -0.233892 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 70 nu = 0.057831 obj = -9.660372, rho = -0.356943 nSV = 8, nBSV = 3 Total nSV = 8 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 82 nu = 0.052797 obj = -11.821240, rho = -0.073032 nSV = 8, nBSV = 2 Total nSV = 8 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 167 nu = 0.046200 obj = -13.896690, rho = -0.423878 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 190 nu = 0.040872 obj = -15.485278, rho = -1.132497 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*.* optimization finished, #iter = 361 nu = 0.031340 obj = -15.673285, rho = -1.473307 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 55 nu = 0.579979 obj = -0.412066, rho = -0.175729 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 95% (95/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 53 nu = 0.506438 obj = -0.512085, rho = -0.101682 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 58 nu = 0.444502 obj = -0.627954, rho = -0.080124 nSV = 47, nBSV = 40 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 65 nu = 0.374581 obj = -0.766360, rho = -0.054915 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 42 nu = 0.316526 obj = -0.932596, rho = -0.026237 nSV = 35, nBSV = 30 Total nSV = 35 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 80 nu = 0.273182 obj = -1.129353, rho = -0.188701 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 45 nu = 0.231828 obj = -1.352279, rho = -0.156925 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 51 nu = 0.186375 obj = -1.622546, rho = -0.133090 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 69 nu = 0.155213 obj = -1.990227, rho = -0.129591 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 153 nu = 0.132709 obj = -2.443597, rho = -0.137539 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 52 nu = 0.115176 obj = -3.009201, rho = -0.090285 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 59 nu = 0.105151 obj = -3.580454, rho = 0.139008 nSV = 13, nBSV = 8 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 138 nu = 0.087224 obj = -4.056563, rho = 0.284494 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 254 nu = 0.068643 obj = -4.516511, rho = 0.245207 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 269 nu = 0.052885 obj = -5.032207, rho = 0.343593 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 146 nu = 0.043728 obj = -5.544337, rho = 0.251084 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.033394 obj = -5.609939, rho = 0.193893 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.023215 obj = -5.609939, rho = 0.193893 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.016139 obj = -5.609939, rho = 0.193893 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.011220 obj = -5.609939, rho = 0.193893 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 49 nu = 0.615181 obj = -0.433592, rho = -0.274533 nSV = 63, nBSV = 57 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 34 nu = 0.536713 obj = -0.533739, rho = -0.325057 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 41 nu = 0.460967 obj = -0.651160, rho = -0.372165 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 55 nu = 0.387354 obj = -0.793350, rho = -0.302535 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.331780 obj = -0.964526, rho = -0.332166 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 68 nu = 0.279947 obj = -1.167272, rho = -0.384782 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 33 nu = 0.243697 obj = -1.401069, rho = -0.272855 nSV = 26, nBSV = 22 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 73 nu = 0.202107 obj = -1.639826, rho = -0.234676 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.163723 obj = -1.926575, rho = -0.254158 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 64 nu = 0.132262 obj = -2.274136, rho = -0.265295 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 72 nu = 0.111902 obj = -2.668497, rho = -0.400301 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.096039 obj = -3.012187, rho = -0.771509 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.073069 obj = -3.296030, rho = -0.832923 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *...* optimization finished, #iter = 397 nu = 0.055927 obj = -3.614509, rho = -0.758788 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*...* optimization finished, #iter = 439 nu = 0.042679 obj = -3.967591, rho = -0.689280 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*........* optimization finished, #iter = 991 nu = 0.032922 obj = -4.342899, rho = -0.650863 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 148 nu = 0.026993 obj = -4.534346, rho = -1.001294 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 148 nu = 0.018765 obj = -4.534346, rho = -1.001294 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 148 nu = 0.013045 obj = -4.534346, rho = -1.001294 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 148 nu = 0.009069 obj = -4.534346, rho = -1.001294 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 44 nu = 0.616785 obj = -0.415735, rho = -0.020572 nSV = 64, nBSV = 58 Total nSV = 64 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.520000 obj = -0.501319, rho = -0.001669 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 35 nu = 0.434640 obj = -0.603675, rho = -0.011981 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 73 nu = 0.361529 obj = -0.725970, rho = 0.004150 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 77 nu = 0.302551 obj = -0.878388, rho = 0.012427 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 44 nu = 0.255796 obj = -1.057609, rho = 0.003246 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 67 nu = 0.220646 obj = -1.254757, rho = 0.009943 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 214 nu = 0.179266 obj = -1.467270, rho = -0.043551 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) ..*..* optimization finished, #iter = 473 nu = 0.144953 obj = -1.729804, rho = -0.018464 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *....* optimization finished, #iter = 406 nu = 0.116824 obj = -2.068567, rho = -0.020190 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *..* optimization finished, #iter = 294 nu = 0.097452 obj = -2.486755, rho = -0.023251 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 131 nu = 0.081649 obj = -2.972458, rho = 0.038620 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 166 nu = 0.068171 obj = -3.579877, rho = 0.224495 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 217 nu = 0.058259 obj = -4.271682, rho = 0.548901 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..*.* optimization finished, #iter = 311 nu = 0.048934 obj = -5.023217, rho = 0.669841 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 205 nu = 0.038773 obj = -5.897875, rho = 0.686743 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*.* optimization finished, #iter = 225 nu = 0.032182 obj = -7.017129, rho = 0.655032 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 162 nu = 0.026413 obj = -8.374598, rho = 0.929927 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) ..*.* optimization finished, #iter = 300 nu = 0.023312 obj = -9.688236, rho = 1.233023 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.1% (951/1000) (classification) ...*.* optimization finished, #iter = 497 nu = 0.020610 obj = -10.307039, rho = 1.239158 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.1% (951/1000) (classification) * optimization finished, #iter = 42 nu = 0.598941 obj = -0.380819, rho = -0.037344 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 54 nu = 0.482853 obj = -0.443263, rho = -0.047391 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 57 nu = 0.394887 obj = -0.517016, rho = -0.035733 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 63 nu = 0.320151 obj = -0.602768, rho = -0.029687 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 90 nu = 0.257297 obj = -0.705431, rho = -0.052993 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 51 nu = 0.209610 obj = -0.830070, rho = -0.030872 nSV = 23, nBSV = 19 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 53 nu = 0.172885 obj = -0.967440, rho = -0.019664 nSV = 20, nBSV = 15 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 34 nu = 0.140264 obj = -1.126135, rho = -0.050778 nSV = 16, nBSV = 11 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 41 nu = 0.116566 obj = -1.298429, rho = -0.091033 nSV = 15, nBSV = 10 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 170 nu = 0.097024 obj = -1.433203, rho = -0.107932 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 168 nu = 0.073635 obj = -1.542068, rho = -0.123678 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 82 nu = 0.056476 obj = -1.632830, rho = -0.060489 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.042092 obj = -1.651396, rho = 0.018855 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.029262 obj = -1.651396, rho = 0.018855 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.020343 obj = -1.651396, rho = 0.018855 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.014142 obj = -1.651396, rho = 0.018855 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.009832 obj = -1.651396, rho = 0.018855 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.006835 obj = -1.651396, rho = 0.018855 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.004752 obj = -1.651396, rho = 0.018855 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.003303 obj = -1.651396, rho = 0.018855 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 44 nu = 0.568694 obj = -0.383780, rho = -0.214226 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 38 nu = 0.480000 obj = -0.462445, rho = -0.234060 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 57 nu = 0.402261 obj = -0.556445, rho = -0.297487 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 31 nu = 0.340000 obj = -0.668937, rho = -0.268622 nSV = 36, nBSV = 32 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 44 nu = 0.286276 obj = -0.790155, rho = -0.255805 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 164 nu = 0.232016 obj = -0.928064, rho = -0.293474 nSV = 29, nBSV = 19 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 63 nu = 0.190506 obj = -1.100135, rho = -0.273198 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.158054 obj = -1.300321, rho = -0.289723 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.127800 obj = -1.539042, rho = -0.222213 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 193 nu = 0.104086 obj = -1.843584, rho = -0.335233 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 152 nu = 0.085600 obj = -2.239328, rho = -0.436961 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.071545 obj = -2.743487, rho = -0.589309 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 168 nu = 0.059878 obj = -3.416924, rho = -0.672643 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 79 nu = 0.052513 obj = -4.286001, rho = -0.854323 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 97 nu = 0.047773 obj = -5.298376, rho = -1.099229 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 162 nu = 0.041984 obj = -6.321591, rho = -0.940251 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 144 nu = 0.037049 obj = -7.276833, rho = -0.924346 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.031449 obj = -7.601086, rho = -1.094281 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.021863 obj = -7.601086, rho = -1.094281 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.015199 obj = -7.601086, rho = -1.094281 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 65 nu = 0.563373 obj = -0.386902, rho = -0.145227 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 42 nu = 0.486676 obj = -0.471225, rho = -0.125045 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 33 nu = 0.400190 obj = -0.570798, rho = -0.129346 nSV = 42, nBSV = 40 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 64 nu = 0.342633 obj = -0.694272, rho = -0.142748 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 29 nu = 0.297362 obj = -0.837610, rho = -0.247681 nSV = 32, nBSV = 28 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 51 nu = 0.250360 obj = -0.991370, rho = -0.127785 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 177 nu = 0.202060 obj = -1.175460, rho = -0.176909 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 98 nu = 0.168239 obj = -1.396670, rho = -0.100989 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *..* optimization finished, #iter = 243 nu = 0.133554 obj = -1.675578, rho = -0.103873 nSV = 20, nBSV = 9 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.111222 obj = -2.066925, rho = -0.069453 nSV = 13, nBSV = 9 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 92 nu = 0.099584 obj = -2.500234, rho = 0.038096 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.081092 obj = -3.005711, rho = 0.051497 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.067850 obj = -3.640992, rho = 0.063272 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 78 nu = 0.059406 obj = -4.436034, rho = -0.028059 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 140 nu = 0.053492 obj = -5.162621, rho = -0.017090 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.043463 obj = -5.655493, rho = -0.016515 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) .* optimization finished, #iter = 181 nu = 0.033986 obj = -5.868917, rho = 0.010323 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) .* optimization finished, #iter = 197 nu = 0.024364 obj = -5.888431, rho = -0.034135 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 95.2% (952/1000) (classification) .* optimization finished, #iter = 197 nu = 0.016938 obj = -5.888431, rho = -0.034135 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 95.2% (952/1000) (classification) .* optimization finished, #iter = 197 nu = 0.011775 obj = -5.888431, rho = -0.034135 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 50 nu = 0.564584 obj = -0.384831, rho = -0.192897 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 36 nu = 0.477122 obj = -0.467073, rho = -0.238441 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 39 nu = 0.403510 obj = -0.566207, rho = -0.209816 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 33 nu = 0.343393 obj = -0.680184, rho = -0.196790 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 43 nu = 0.288496 obj = -0.815304, rho = -0.139242 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 38 nu = 0.239808 obj = -0.971735, rho = -0.134650 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 63 nu = 0.195986 obj = -1.163096, rho = -0.077451 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.165602 obj = -1.391090, rho = 0.019927 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) .* optimization finished, #iter = 183 nu = 0.137212 obj = -1.650490, rho = 0.055429 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.110826 obj = -1.988293, rho = 0.092511 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 74 nu = 0.092876 obj = -2.418046, rho = 0.190369 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) .**..* optimization finished, #iter = 339 nu = 0.076815 obj = -2.966869, rho = 0.216810 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 140 nu = 0.066062 obj = -3.700695, rho = 0.186744 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.060258 obj = -4.487783, rho = 0.379381 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 207 nu = 0.049959 obj = -5.338342, rho = 0.335389 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 151 nu = 0.042548 obj = -6.380651, rho = 0.325090 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 152 nu = 0.038287 obj = -7.150069, rho = 0.509080 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) .* optimization finished, #iter = 197 nu = 0.030086 obj = -7.271851, rho = 0.596926 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) .* optimization finished, #iter = 197 nu = 0.020916 obj = -7.271851, rho = 0.596926 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) .* optimization finished, #iter = 197 nu = 0.014540 obj = -7.271851, rho = 0.596926 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 41 nu = 0.609104 obj = -0.435337, rho = -0.213385 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 97% (97/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 63 nu = 0.534028 obj = -0.539366, rho = -0.225208 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 46 nu = 0.462025 obj = -0.662004, rho = -0.252225 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 36 nu = 0.394132 obj = -0.814139, rho = -0.203027 nSV = 41, nBSV = 37 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 31 nu = 0.339449 obj = -0.996089, rho = -0.160335 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 59 nu = 0.291504 obj = -1.207730, rho = -0.052784 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 55 nu = 0.245055 obj = -1.460259, rho = -0.050247 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 85 nu = 0.210095 obj = -1.752146, rho = 0.054428 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 151 nu = 0.180130 obj = -2.045436, rho = 0.126456 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 142 nu = 0.144019 obj = -2.363713, rho = 0.166135 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 138 nu = 0.118167 obj = -2.696123, rho = 0.274596 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.097369 obj = -3.030143, rho = 0.205498 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 178 nu = 0.075103 obj = -3.269320, rho = 0.147628 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 193 nu = 0.057659 obj = -3.468366, rho = 0.040490 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 253 nu = 0.042276 obj = -3.601647, rho = -0.019946 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 175 nu = 0.031197 obj = -3.643349, rho = -0.113686 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 175 nu = 0.021688 obj = -3.643349, rho = -0.113686 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 175 nu = 0.015077 obj = -3.643349, rho = -0.113686 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 175 nu = 0.010481 obj = -3.643349, rho = -0.113686 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 175 nu = 0.007287 obj = -3.643349, rho = -0.113686 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 51 nu = 0.559941 obj = -0.384750, rho = 0.063778 nSV = 60, nBSV = 53 Total nSV = 60 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.493014 obj = -0.463602, rho = 0.152174 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 55 nu = 0.403181 obj = -0.550280, rho = 0.140280 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 56 nu = 0.340000 obj = -0.655524, rho = 0.070736 nSV = 36, nBSV = 32 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 54 nu = 0.278044 obj = -0.770594, rho = 0.003491 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 58 nu = 0.228290 obj = -0.904361, rho = 0.007449 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 75 nu = 0.198846 obj = -1.041766, rho = 0.101045 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 160 nu = 0.154800 obj = -1.162088, rho = 0.167088 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 129 nu = 0.119380 obj = -1.304824, rho = 0.171211 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 70 nu = 0.094544 obj = -1.459194, rho = 0.204767 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 86 nu = 0.072791 obj = -1.629832, rho = 0.229176 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 96 nu = 0.057035 obj = -1.833272, rho = 0.287072 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 138 nu = 0.043775 obj = -2.059910, rho = 0.314302 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.037529 obj = -2.253104, rho = 0.150938 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 73 nu = 0.027947 obj = -2.269429, rho = 0.043123 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 73 nu = 0.019429 obj = -2.269429, rho = 0.043123 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 73 nu = 0.013507 obj = -2.269429, rho = 0.043123 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 73 nu = 0.009390 obj = -2.269429, rho = 0.043123 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 73 nu = 0.006528 obj = -2.269429, rho = 0.043123 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 73 nu = 0.004538 obj = -2.269429, rho = 0.043123 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 40 nu = 0.629928 obj = -0.440823, rho = -0.204817 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 35 nu = 0.548885 obj = -0.539596, rho = -0.266590 nSV = 56, nBSV = 53 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.471090 obj = -0.655681, rho = -0.303237 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 53 nu = 0.397896 obj = -0.784244, rho = -0.217258 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 53 nu = 0.332089 obj = -0.936829, rho = -0.307796 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 90 nu = 0.269928 obj = -1.125769, rho = -0.300736 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.221028 obj = -1.375874, rho = -0.345870 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 66 nu = 0.185235 obj = -1.715497, rho = -0.389095 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 249 nu = 0.164101 obj = -2.138983, rho = -0.567756 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 287 nu = 0.143032 obj = -2.639150, rho = -0.611666 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 172 nu = 0.122018 obj = -3.251691, rho = -0.681079 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 190 nu = 0.102197 obj = -4.007923, rho = -0.694319 nSV = 18, nBSV = 7 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 88 nu = 0.093778 obj = -4.947409, rho = -1.044861 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) ...*..* optimization finished, #iter = 581 nu = 0.085872 obj = -5.754886, rho = -1.258216 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) ..*...* optimization finished, #iter = 559 nu = 0.069576 obj = -6.393094, rho = -1.608368 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) ..........**.* optimization finished, #iter = 1097 nu = 0.055119 obj = -6.747382, rho = -1.627185 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.4% (944/1000) (classification) .....* optimization finished, #iter = 576 nu = 0.041126 obj = -6.909793, rho = -1.641154 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94% (940/1000) (classification) .....* optimization finished, #iter = 576 nu = 0.028590 obj = -6.909793, rho = -1.641154 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94% (940/1000) (classification) .....* optimization finished, #iter = 576 nu = 0.019876 obj = -6.909793, rho = -1.641154 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94% (940/1000) (classification) .....* optimization finished, #iter = 576 nu = 0.013818 obj = -6.909793, rho = -1.641154 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94% (940/1000) (classification) * optimization finished, #iter = 49 nu = 0.542887 obj = -0.369776, rho = -0.213619 nSV = 57, nBSV = 51 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 38 nu = 0.463226 obj = -0.446866, rho = -0.121906 nSV = 48, nBSV = 45 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 42 nu = 0.389743 obj = -0.537593, rho = -0.185814 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 64 nu = 0.324778 obj = -0.645211, rho = -0.211435 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 60 nu = 0.267432 obj = -0.778484, rho = -0.243086 nSV = 32, nBSV = 22 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 35 nu = 0.233500 obj = -0.941346, rho = -0.126793 nSV = 26, nBSV = 22 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 75 nu = 0.195028 obj = -1.097712, rho = -0.104927 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 88 nu = 0.157209 obj = -1.289578, rho = -0.166931 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 176 nu = 0.127599 obj = -1.518816, rho = -0.227776 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 77 nu = 0.103346 obj = -1.804346, rho = -0.271353 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 168 nu = 0.088548 obj = -2.133595, rho = -0.292830 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..* optimization finished, #iter = 268 nu = 0.069625 obj = -2.520121, rho = -0.311963 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) ...*..* optimization finished, #iter = 523 nu = 0.056651 obj = -3.044181, rho = -0.355297 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*....* optimization finished, #iter = 506 nu = 0.049270 obj = -3.658547, rho = -0.376277 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*..* optimization finished, #iter = 373 nu = 0.042266 obj = -4.309958, rho = -0.412011 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 207 nu = 0.037264 obj = -4.883635, rho = -0.701518 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) ....* optimization finished, #iter = 487 nu = 0.029761 obj = -4.999626, rho = -0.889354 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) ....* optimization finished, #iter = 487 nu = 0.020690 obj = -4.999626, rho = -0.889354 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) ....* optimization finished, #iter = 487 nu = 0.014383 obj = -4.999626, rho = -0.889354 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) ....* optimization finished, #iter = 487 nu = 0.009999 obj = -4.999626, rho = -0.889354 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 42 nu = 0.570633 obj = -0.384205, rho = -0.246089 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 57 nu = 0.479606 obj = -0.461275, rho = -0.205866 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 70 nu = 0.395872 obj = -0.556139, rho = -0.165155 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.330855 obj = -0.676106, rho = -0.168049 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 50 nu = 0.280000 obj = -0.823138, rho = -0.108812 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 65 nu = 0.238183 obj = -1.000915, rho = -0.020589 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 79 nu = 0.200205 obj = -1.214137, rho = 0.027338 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *..* optimization finished, #iter = 280 nu = 0.177857 obj = -1.447601, rho = 0.121617 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 153 nu = 0.140282 obj = -1.711774, rho = 0.082399 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 169 nu = 0.113166 obj = -2.076119, rho = 0.046237 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.098242 obj = -2.536256, rho = 0.016091 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 143 nu = 0.084079 obj = -3.023039, rho = 0.068105 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 160 nu = 0.071214 obj = -3.595775, rho = -0.143166 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..*.* optimization finished, #iter = 341 nu = 0.061038 obj = -4.134734, rho = -0.518754 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 191 nu = 0.049729 obj = -4.589819, rho = -0.708106 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*....* optimization finished, #iter = 507 nu = 0.040069 obj = -4.881211, rho = -0.816830 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ....* optimization finished, #iter = 481 nu = 0.029305 obj = -4.923778, rho = -0.745492 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ....* optimization finished, #iter = 481 nu = 0.020373 obj = -4.923778, rho = -0.745492 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ....* optimization finished, #iter = 481 nu = 0.014163 obj = -4.923778, rho = -0.745492 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ....* optimization finished, #iter = 481 nu = 0.009846 obj = -4.923778, rho = -0.745492 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 54 nu = 0.631289 obj = -0.422440, rho = -0.262493 nSV = 66, nBSV = 61 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 55 nu = 0.539885 obj = -0.503110, rho = -0.230793 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 71 nu = 0.436194 obj = -0.597882, rho = -0.214811 nSV = 47, nBSV = 40 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 45 nu = 0.365108 obj = -0.714370, rho = -0.195523 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 79 nu = 0.304778 obj = -0.846500, rho = -0.210946 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 39 nu = 0.251897 obj = -0.995460, rho = -0.271236 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 68 nu = 0.209427 obj = -1.159516, rho = -0.209639 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.172918 obj = -1.317901, rho = -0.216845 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 92 nu = 0.137988 obj = -1.490409, rho = -0.222491 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 122 nu = 0.110162 obj = -1.648483, rho = -0.293429 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 149 nu = 0.085157 obj = -1.793450, rho = -0.299553 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 179 nu = 0.064621 obj = -1.914774, rho = -0.253423 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 129 nu = 0.049738 obj = -1.951920, rho = -0.222463 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 129 nu = 0.034578 obj = -1.951920, rho = -0.222463 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 129 nu = 0.024038 obj = -1.951920, rho = -0.222463 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 129 nu = 0.016711 obj = -1.951920, rho = -0.222463 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 129 nu = 0.011617 obj = -1.951920, rho = -0.222463 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 129 nu = 0.008076 obj = -1.951920, rho = -0.222463 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 129 nu = 0.005615 obj = -1.951920, rho = -0.222463 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 129 nu = 0.003903 obj = -1.951920, rho = -0.222463 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 56 nu = 0.564462 obj = -0.386398, rho = -0.276209 nSV = 61, nBSV = 54 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 39 nu = 0.481412 obj = -0.468147, rho = -0.274929 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 32 nu = 0.404945 obj = -0.567547, rho = -0.258633 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 35 nu = 0.344200 obj = -0.682512, rho = -0.328750 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.294710 obj = -0.808164, rho = -0.240022 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 85 nu = 0.241344 obj = -0.944441, rho = -0.161695 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 69 nu = 0.199935 obj = -1.094529, rho = -0.172552 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) .*.* optimization finished, #iter = 212 nu = 0.157039 obj = -1.257397, rho = -0.233457 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 69 nu = 0.126645 obj = -1.463880, rho = -0.261196 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 70 nu = 0.105991 obj = -1.692114, rho = -0.452659 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.082945 obj = -1.922752, rho = -0.522513 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 82 nu = 0.066165 obj = -2.214466, rho = -0.602955 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.057005 obj = -2.440249, rho = -0.771548 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 168 nu = 0.043892 obj = -2.477534, rho = -0.761245 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 168 nu = 0.030514 obj = -2.477534, rho = -0.761245 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 168 nu = 0.021213 obj = -2.477534, rho = -0.761245 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 168 nu = 0.014747 obj = -2.477534, rho = -0.761245 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 168 nu = 0.010252 obj = -2.477534, rho = -0.761245 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 168 nu = 0.007127 obj = -2.477534, rho = -0.761245 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 168 nu = 0.004955 obj = -2.477534, rho = -0.761245 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 55 nu = 0.626987 obj = -0.423057, rho = -0.023673 nSV = 67, nBSV = 60 Total nSV = 67 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 37 nu = 0.531549 obj = -0.511278, rho = -0.044607 nSV = 54, nBSV = 52 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 36 nu = 0.449539 obj = -0.612164, rho = -0.090513 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 40 nu = 0.381829 obj = -0.720855, rho = -0.018258 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 57 nu = 0.304933 obj = -0.846129, rho = 0.003194 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 63 nu = 0.250454 obj = -0.994244, rho = -0.089059 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 76 nu = 0.204244 obj = -1.177322, rho = -0.107900 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 40 nu = 0.169998 obj = -1.377312, rho = -0.153296 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 87 nu = 0.135038 obj = -1.611263, rho = -0.116937 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 73 nu = 0.114056 obj = -1.909917, rho = -0.115372 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.094348 obj = -2.204623, rho = -0.254802 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 138 nu = 0.078068 obj = -2.476010, rho = -0.334168 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.....* optimization finished, #iter = 646 nu = 0.061910 obj = -2.684964, rho = -0.184341 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.047411 obj = -2.862502, rho = -0.072529 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.035840 obj = -2.909679, rho = 0.041516 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.024915 obj = -2.909679, rho = 0.041516 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.017321 obj = -2.909679, rho = 0.041516 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.012041 obj = -2.909679, rho = 0.041516 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.008371 obj = -2.909679, rho = 0.041516 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.005820 obj = -2.909679, rho = 0.041516 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 55 nu = 0.607132 obj = -0.396856, rho = -0.130432 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 51 nu = 0.500104 obj = -0.469482, rho = -0.146569 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 32 nu = 0.411676 obj = -0.559515, rho = -0.141011 nSV = 42, nBSV = 39 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 38 nu = 0.339241 obj = -0.666367, rho = -0.157489 nSV = 36, nBSV = 32 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 52 nu = 0.293711 obj = -0.792439, rho = -0.169282 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 57 nu = 0.243595 obj = -0.908934, rho = -0.087941 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 48 nu = 0.193227 obj = -1.032992, rho = -0.054924 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 99 nu = 0.150951 obj = -1.168216, rho = -0.086631 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) ...*..* optimization finished, #iter = 540 nu = 0.118770 obj = -1.318553, rho = -0.070186 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 170 nu = 0.092967 obj = -1.499485, rho = -0.009738 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 205 nu = 0.072369 obj = -1.729932, rho = 0.017463 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 77 nu = 0.060785 obj = -2.007957, rho = -0.023112 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 79 nu = 0.047638 obj = -2.273262, rho = -0.086810 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 170 nu = 0.037959 obj = -2.587871, rho = -0.122569 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.032528 obj = -2.847225, rho = -0.231913 nSV = 7, nBSV = 1 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.024633 obj = -2.876883, rho = -0.282247 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.017125 obj = -2.876883, rho = -0.282247 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.011905 obj = -2.876883, rho = -0.282247 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.008276 obj = -2.876883, rho = -0.282247 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.005754 obj = -2.876883, rho = -0.282247 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 42 nu = 0.577893 obj = -0.404220, rho = 0.062147 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 97% (97/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 40 nu = 0.494381 obj = -0.499583, rho = 0.049619 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 97% (97/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 52 nu = 0.430040 obj = -0.610641, rho = 0.077824 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 57 nu = 0.356417 obj = -0.747700, rho = 0.047683 nSV = 38, nBSV = 33 Total nSV = 38 Accuracy = 97% (97/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 75 nu = 0.302351 obj = -0.927938, rho = 0.031741 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 97% (97/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 60 nu = 0.269547 obj = -1.141591, rho = 0.236839 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 69 nu = 0.225046 obj = -1.405313, rho = 0.243372 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 67 nu = 0.192667 obj = -1.741036, rho = 0.182075 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 94.7% (947/1000) (classification) * optimization finished, #iter = 80 nu = 0.164422 obj = -2.158156, rho = 0.218940 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 94.4% (944/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.140643 obj = -2.698748, rho = 0.087198 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 94.4% (944/1000) (classification) .* optimization finished, #iter = 160 nu = 0.119314 obj = -3.416453, rho = 0.143962 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 94.5% (945/1000) (classification) * optimization finished, #iter = 58 nu = 0.103849 obj = -4.404514, rho = 0.158580 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 95 nu = 0.098374 obj = -5.653957, rho = 0.410059 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 97% (97/100) (classification) Accuracy = 93.8% (938/1000) (classification) * optimization finished, #iter = 90 nu = 0.084057 obj = -7.192337, rho = 0.396225 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 97% (97/100) (classification) Accuracy = 94.7% (947/1000) (classification) * optimization finished, #iter = 95 nu = 0.078138 obj = -9.115561, rho = 0.165191 nSV = 11, nBSV = 5 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.4% (954/1000) (classification) .*.* optimization finished, #iter = 268 nu = 0.073509 obj = -11.061302, rho = -0.086599 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 94.5% (945/1000) (classification) .*.* optimization finished, #iter = 270 nu = 0.065415 obj = -12.439530, rho = -0.585012 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.1% (941/1000) (classification) .* optimization finished, #iter = 193 nu = 0.051793 obj = -13.428277, rho = -0.932627 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 93.5% (935/1000) (classification) ..*..* optimization finished, #iter = 441 nu = 0.038961 obj = -13.545737, rho = -1.070896 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 93.2% (932/1000) (classification) ..*..* optimization finished, #iter = 441 nu = 0.027085 obj = -13.545737, rho = -1.070896 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 93.2% (932/1000) (classification) * optimization finished, #iter = 35 nu = 0.576404 obj = -0.396179, rho = -0.046816 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 40 nu = 0.495638 obj = -0.482417, rho = 0.021851 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 29 nu = 0.421047 obj = -0.580237, rho = 0.093593 nSV = 44, nBSV = 41 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 56 nu = 0.351802 obj = -0.692002, rho = 0.098912 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 54 nu = 0.294927 obj = -0.824225, rho = 0.128478 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 150 nu = 0.243484 obj = -0.979302, rho = 0.113667 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 79 nu = 0.200899 obj = -1.165481, rho = 0.045926 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.165176 obj = -1.392052, rho = 0.015498 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*.* optimization finished, #iter = 347 nu = 0.134973 obj = -1.662585, rho = 0.009676 nSV = 19, nBSV = 8 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) *......* optimization finished, #iter = 660 nu = 0.109275 obj = -2.032689, rho = -0.005736 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 154 nu = 0.091965 obj = -2.534102, rho = -0.104027 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 91 nu = 0.081015 obj = -3.200909, rho = -0.152040 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.073005 obj = -3.939847, rho = -0.310316 nSV = 10, nBSV = 4 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .*.* optimization finished, #iter = 231 nu = 0.061523 obj = -4.778799, rho = -0.401597 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 168 nu = 0.058695 obj = -5.615295, rho = -0.593275 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .*.* optimization finished, #iter = 270 nu = 0.049958 obj = -5.925612, rho = -0.822250 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..* optimization finished, #iter = 298 nu = 0.035290 obj = -5.928006, rho = -0.866629 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) ..* optimization finished, #iter = 298 nu = 0.024534 obj = -5.928006, rho = -0.866629 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) ..* optimization finished, #iter = 298 nu = 0.017056 obj = -5.928006, rho = -0.866629 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) ..* optimization finished, #iter = 298 nu = 0.011857 obj = -5.928006, rho = -0.866629 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 59 nu = 0.595593 obj = -0.415601, rho = -0.130242 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 51 nu = 0.516963 obj = -0.512039, rho = -0.106690 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 41 nu = 0.438037 obj = -0.628574, rho = -0.065828 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 51 nu = 0.381297 obj = -0.757268, rho = 0.035327 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 43 nu = 0.321014 obj = -0.903582, rho = 0.016909 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 84 nu = 0.264992 obj = -1.070808, rho = -0.019623 nSV = 32, nBSV = 23 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 183 nu = 0.217292 obj = -1.279114, rho = -0.056729 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 58 nu = 0.175200 obj = -1.549620, rho = -0.058168 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 49 nu = 0.147389 obj = -1.904347, rho = -0.100926 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.130454 obj = -2.327011, rho = -0.141612 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 105 nu = 0.109162 obj = -2.840068, rho = -0.115151 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 140 nu = 0.097152 obj = -3.418124, rho = -0.022803 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 148 nu = 0.081910 obj = -3.973601, rho = -0.118878 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 127 nu = 0.068405 obj = -4.495496, rho = 0.086862 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 99 nu = 0.054865 obj = -4.952843, rho = 0.118907 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) .*.* optimization finished, #iter = 205 nu = 0.041631 obj = -5.263175, rho = 0.169945 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 156 nu = 0.032232 obj = -5.414912, rho = 0.207110 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 156 nu = 0.022407 obj = -5.414912, rho = 0.207110 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 156 nu = 0.015577 obj = -5.414912, rho = 0.207110 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 156 nu = 0.010829 obj = -5.414912, rho = 0.207110 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 40 nu = 0.601252 obj = -0.432091, rho = -0.120366 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 38 nu = 0.518415 obj = -0.540873, rho = -0.168305 nSV = 53, nBSV = 49 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 91 nu = 0.445861 obj = -0.682030, rho = -0.156072 nSV = 49, nBSV = 42 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 38 nu = 0.404481 obj = -0.862218, rho = -0.058434 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 67 nu = 0.355770 obj = -1.070340, rho = -0.020251 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 60 nu = 0.301865 obj = -1.324971, rho = 0.004853 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 97 nu = 0.258395 obj = -1.650333, rho = 0.103520 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 79 nu = 0.222822 obj = -2.064498, rho = 0.160371 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 96% (96/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.190941 obj = -2.610902, rho = 0.105667 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 96% (96/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 181 nu = 0.167082 obj = -3.336050, rho = 0.193558 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 96% (96/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 194 nu = 0.145078 obj = -4.301799, rho = 0.247512 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 96% (96/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 89 nu = 0.129592 obj = -5.633956, rho = 0.272463 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.120615 obj = -7.332598, rho = 0.381520 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) ..*.* optimization finished, #iter = 388 nu = 0.109190 obj = -9.415444, rho = 0.391374 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 201 nu = 0.098268 obj = -12.162437, rho = 0.333564 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*..* optimization finished, #iter = 420 nu = 0.086612 obj = -15.587029, rho = 0.398480 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) ...*.* optimization finished, #iter = 410 nu = 0.076219 obj = -20.250014, rho = 0.500251 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*..........* optimization finished, #iter = 1200 nu = 0.067749 obj = -26.737024, rho = 0.550517 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) ...*.* optimization finished, #iter = 417 nu = 0.063983 obj = -35.350759, rho = 0.760930 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) ....*.* optimization finished, #iter = 545 nu = 0.061246 obj = -45.546664, rho = 1.265298 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 54 nu = 0.648020 obj = -0.448282, rho = -0.069456 nSV = 67, nBSV = 63 Total nSV = 67 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 52 nu = 0.549606 obj = -0.546314, rho = -0.106468 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 57 nu = 0.470761 obj = -0.668285, rho = -0.092374 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.404736 obj = -0.813383, rho = -0.020619 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 32 nu = 0.353344 obj = -0.973513, rho = -0.116771 nSV = 37, nBSV = 33 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 59 nu = 0.291350 obj = -1.139834, rho = -0.151105 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 69 nu = 0.235561 obj = -1.328122, rho = -0.156442 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 170 nu = 0.193625 obj = -1.554891, rho = -0.278948 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) ..*...* optimization finished, #iter = 583 nu = 0.155358 obj = -1.804732, rho = -0.367492 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) ..* optimization finished, #iter = 299 nu = 0.122925 obj = -2.123841, rho = -0.418197 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) ..* optimization finished, #iter = 245 nu = 0.100535 obj = -2.540050, rho = -0.526733 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*....* optimization finished, #iter = 591 nu = 0.087645 obj = -2.986207, rho = -0.684786 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 186 nu = 0.071975 obj = -3.415777, rho = -0.673830 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 281 nu = 0.059040 obj = -3.807729, rho = -0.702686 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) ......*...* optimization finished, #iter = 989 nu = 0.045638 obj = -4.177737, rho = -0.732278 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..* optimization finished, #iter = 279 nu = 0.036010 obj = -4.490624, rho = -0.888993 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.026942 obj = -4.526408, rho = -0.961999 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.018730 obj = -4.526408, rho = -0.961999 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.013021 obj = -4.526408, rho = -0.961999 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.009052 obj = -4.526408, rho = -0.961999 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 38 nu = 0.620804 obj = -0.429265, rho = -0.265752 nSV = 65, nBSV = 60 Total nSV = 65 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 45 nu = 0.532506 obj = -0.523152, rho = -0.242406 nSV = 55, nBSV = 48 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 41 nu = 0.455055 obj = -0.634828, rho = -0.192342 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 42 nu = 0.380000 obj = -0.772894, rho = -0.169274 nSV = 40, nBSV = 36 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 61 nu = 0.316043 obj = -0.942068, rho = -0.177461 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 28 nu = 0.273130 obj = -1.158956, rho = -0.195342 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 28 nu = 0.241438 obj = -1.401279, rho = -0.321676 nSV = 27, nBSV = 23 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 70 nu = 0.200300 obj = -1.647279, rho = -0.367806 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.167618 obj = -1.928475, rho = -0.453914 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 90 nu = 0.133854 obj = -2.259019, rho = -0.358300 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.109537 obj = -2.651400, rho = -0.294611 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.092552 obj = -3.016844, rho = -0.486043 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.073100 obj = -3.387430, rho = -0.644633 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 161 nu = 0.059287 obj = -3.717757, rho = -0.552450 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 159 nu = 0.047506 obj = -3.857324, rho = -0.544522 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 170 nu = 0.033025 obj = -3.857324, rho = -0.544875 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 170 nu = 0.022959 obj = -3.857324, rho = -0.544875 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 170 nu = 0.015961 obj = -3.857324, rho = -0.544875 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 170 nu = 0.011096 obj = -3.857324, rho = -0.544875 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 170 nu = 0.007714 obj = -3.857324, rho = -0.544875 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 48 nu = 0.591587 obj = -0.391451, rho = -0.008620 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 42 nu = 0.495861 obj = -0.468403, rho = 0.015877 nSV = 50, nBSV = 47 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 64 nu = 0.405901 obj = -0.559107, rho = -0.003632 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 58 nu = 0.336448 obj = -0.674181, rho = -0.060148 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 33 nu = 0.286260 obj = -0.811504, rho = -0.170819 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.233384 obj = -0.973360, rho = -0.166949 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 31 nu = 0.192844 obj = -1.181147, rho = -0.215605 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 27 nu = 0.166033 obj = -1.444332, rho = -0.155653 nSV = 20, nBSV = 15 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 62 nu = 0.147761 obj = -1.721042, rho = -0.043832 nSV = 17, nBSV = 12 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 92 nu = 0.124147 obj = -1.974306, rho = 0.057103 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) ...* optimization finished, #iter = 381 nu = 0.096669 obj = -2.237984, rho = 0.090479 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..*.* optimization finished, #iter = 382 nu = 0.077376 obj = -2.537626, rho = 0.143027 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 244 nu = 0.060809 obj = -2.860987, rho = 0.191445 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 269 nu = 0.047813 obj = -3.215335, rho = 0.111524 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.038636 obj = -3.576968, rho = 0.074374 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 234 nu = 0.032130 obj = -3.753155, rho = 0.040869 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 234 nu = 0.022336 obj = -3.753155, rho = 0.040869 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 234 nu = 0.015528 obj = -3.753155, rho = 0.040869 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 234 nu = 0.010795 obj = -3.753155, rho = 0.040869 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 234 nu = 0.007505 obj = -3.753155, rho = 0.040869 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 41 nu = 0.680000 obj = -0.471488, rho = -0.184057 nSV = 70, nBSV = 67 Total nSV = 70 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 38 nu = 0.584049 obj = -0.575992, rho = -0.220450 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 53 nu = 0.498177 obj = -0.698423, rho = -0.178446 nSV = 53, nBSV = 46 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 63 nu = 0.417066 obj = -0.845208, rho = -0.284967 nSV = 48, nBSV = 40 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 35 nu = 0.352948 obj = -1.028212, rho = -0.357017 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 83 nu = 0.295684 obj = -1.243137, rho = -0.371887 nSV = 34, nBSV = 25 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 93 nu = 0.249665 obj = -1.511073, rho = -0.511352 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.205233 obj = -1.857875, rho = -0.531440 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 85 nu = 0.174158 obj = -2.324323, rho = -0.502005 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 90 nu = 0.146931 obj = -2.946235, rho = -0.528072 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 120 nu = 0.130403 obj = -3.785306, rho = -0.583162 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 162 nu = 0.119259 obj = -4.850391, rho = -0.719052 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.104939 obj = -6.160343, rho = -0.967798 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 187 nu = 0.094205 obj = -7.804205, rho = -1.305384 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.083638 obj = -9.857838, rho = -1.571561 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 137 nu = 0.078132 obj = -12.034491, rho = -2.003366 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.1% (961/1000) (classification) .*.....* optimization finished, #iter = 655 nu = 0.071241 obj = -13.852062, rho = -2.444792 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.2% (952/1000) (classification) ....*..* optimization finished, #iter = 665 nu = 0.058871 obj = -14.701265, rho = -3.138302 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.3% (943/1000) (classification) .....*.* optimization finished, #iter = 680 nu = 0.042652 obj = -14.828111, rho = -3.337827 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.2% (942/1000) (classification) .....*.* optimization finished, #iter = 680 nu = 0.029651 obj = -14.828111, rho = -3.337827 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.2% (942/1000) (classification) * optimization finished, #iter = 75 nu = 0.603203 obj = -0.424514, rho = 0.027661 nSV = 64, nBSV = 57 Total nSV = 64 Accuracy = 96% (96/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 39 nu = 0.515627 obj = -0.525016, rho = 0.035997 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.442393 obj = -0.648853, rho = -0.004552 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 48 nu = 0.389789 obj = -0.793357, rho = -0.069343 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.331205 obj = -0.963916, rho = -0.069411 nSV = 37, nBSV = 28 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 75 nu = 0.274866 obj = -1.179453, rho = -0.075862 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 55 nu = 0.234547 obj = -1.455704, rho = 0.010723 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 85 nu = 0.195498 obj = -1.803691, rho = 0.032454 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 84 nu = 0.166699 obj = -2.280586, rho = 0.072016 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 78 nu = 0.145300 obj = -2.906098, rho = 0.039285 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 84 nu = 0.131067 obj = -3.693644, rho = -0.086128 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.110828 obj = -4.749503, rho = -0.104990 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 93 nu = 0.097772 obj = -6.224523, rho = -0.168661 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 67 nu = 0.091377 obj = -8.208023, rho = -0.247282 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 78 nu = 0.087499 obj = -10.619824, rho = -0.153871 nSV = 11, nBSV = 6 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 120 nu = 0.080594 obj = -13.302190, rho = -0.033941 nSV = 11, nBSV = 5 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 152 nu = 0.072061 obj = -16.267622, rho = 0.226055 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 184 nu = 0.065227 obj = -19.113338, rho = 0.705503 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..*.* optimization finished, #iter = 317 nu = 0.058237 obj = -20.790273, rho = 0.867599 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ....*.* optimization finished, #iter = 552 nu = 0.041609 obj = -20.809726, rho = 0.870089 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 36 nu = 0.604754 obj = -0.406923, rho = -0.026266 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 85 nu = 0.505491 obj = -0.489000, rho = -0.005419 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 28 nu = 0.431162 obj = -0.590917, rho = -0.056588 nSV = 44, nBSV = 41 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 90 nu = 0.364875 obj = -0.699611, rho = 0.055042 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 44 nu = 0.300396 obj = -0.819175, rho = 0.091084 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 36 nu = 0.252608 obj = -0.951786, rho = -0.006367 nSV = 27, nBSV = 23 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 65 nu = 0.201237 obj = -1.079354, rho = 0.020118 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *..........* optimization finished, #iter = 1047 nu = 0.158360 obj = -1.212011, rho = 0.056450 nSV = 22, nBSV = 10 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 193 nu = 0.127211 obj = -1.370363, rho = -0.044690 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 72 nu = 0.103852 obj = -1.485168, rho = -0.059708 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.075417 obj = -1.576452, rho = -0.083462 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 184 nu = 0.057795 obj = -1.663578, rho = -0.213662 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 214 nu = 0.042495 obj = -1.708999, rho = -0.244719 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 251 nu = 0.030358 obj = -1.713650, rho = -0.238407 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 251 nu = 0.021105 obj = -1.713650, rho = -0.238407 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 251 nu = 0.014672 obj = -1.713650, rho = -0.238407 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 251 nu = 0.010200 obj = -1.713650, rho = -0.238407 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 251 nu = 0.007091 obj = -1.713650, rho = -0.238407 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 251 nu = 0.004930 obj = -1.713650, rho = -0.238407 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 251 nu = 0.003427 obj = -1.713650, rho = -0.238407 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 50 nu = 0.600000 obj = -0.404767, rho = -0.101951 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 43 nu = 0.496247 obj = -0.490087, rho = -0.098106 nSV = 54, nBSV = 46 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 49 nu = 0.426699 obj = -0.596901, rho = -0.100661 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 40 nu = 0.365426 obj = -0.718134, rho = -0.074604 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 87 nu = 0.304941 obj = -0.843053, rho = -0.062960 nSV = 36, nBSV = 27 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 87 nu = 0.249116 obj = -0.994676, rho = -0.030866 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.203136 obj = -1.182241, rho = -0.007712 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.169650 obj = -1.387125, rho = 0.069359 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 195 nu = 0.141727 obj = -1.619724, rho = 0.025681 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 146 nu = 0.116272 obj = -1.843649, rho = 0.063109 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 218 nu = 0.090282 obj = -2.097499, rho = -0.028691 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..*.* optimization finished, #iter = 362 nu = 0.071965 obj = -2.399037, rho = 0.067870 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..* optimization finished, #iter = 272 nu = 0.056512 obj = -2.755123, rho = 0.133538 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ....*.* optimization finished, #iter = 553 nu = 0.051476 obj = -3.040698, rho = 0.629169 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ..*.* optimization finished, #iter = 313 nu = 0.037573 obj = -3.141339, rho = 0.631607 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) ..*..* optimization finished, #iter = 415 nu = 0.027105 obj = -3.165685, rho = 0.631346 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) ..*..* optimization finished, #iter = 415 nu = 0.018843 obj = -3.165685, rho = 0.631346 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) ..*..* optimization finished, #iter = 415 nu = 0.013100 obj = -3.165685, rho = 0.631346 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) ..*..* optimization finished, #iter = 415 nu = 0.009107 obj = -3.165685, rho = 0.631346 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) ..*..* optimization finished, #iter = 415 nu = 0.006331 obj = -3.165685, rho = 0.631346 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 36 nu = 0.647127 obj = -0.451275, rho = 0.017834 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 41 nu = 0.560000 obj = -0.551762, rho = 0.046589 nSV = 58, nBSV = 54 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 92 nu = 0.481311 obj = -0.666799, rho = 0.030038 nSV = 52, nBSV = 44 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 45 nu = 0.404498 obj = -0.804219, rho = -0.004863 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 70 nu = 0.336362 obj = -0.962752, rho = -0.016277 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 73 nu = 0.280845 obj = -1.159214, rho = -0.054144 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 98 nu = 0.237383 obj = -1.391471, rho = -0.030067 nSV = 26, nBSV = 21 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 156 nu = 0.203392 obj = -1.644545, rho = 0.123834 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 194 nu = 0.166931 obj = -1.906350, rho = 0.138053 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) .*.* optimization finished, #iter = 268 nu = 0.132236 obj = -2.201631, rho = 0.095146 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) ..*..* optimization finished, #iter = 431 nu = 0.107766 obj = -2.569039, rho = 0.098998 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*.* optimization finished, #iter = 397 nu = 0.084624 obj = -3.017469, rho = 0.080296 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*.* optimization finished, #iter = 218 nu = 0.072525 obj = -3.567929, rho = 0.024065 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .*.* optimization finished, #iter = 224 nu = 0.065564 obj = -3.965456, rho = -0.310440 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) ...* optimization finished, #iter = 392 nu = 0.049305 obj = -4.002797, rho = -0.391876 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) ...* optimization finished, #iter = 392 nu = 0.034276 obj = -4.002797, rho = -0.391876 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) ...* optimization finished, #iter = 392 nu = 0.023829 obj = -4.002797, rho = -0.391876 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) ...* optimization finished, #iter = 392 nu = 0.016566 obj = -4.002797, rho = -0.391876 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) ...* optimization finished, #iter = 392 nu = 0.011516 obj = -4.002797, rho = -0.391876 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) ...* optimization finished, #iter = 392 nu = 0.008006 obj = -4.002797, rho = -0.391876 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 59 nu = 0.591439 obj = -0.402086, rho = -0.109426 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 40 nu = 0.507739 obj = -0.484127, rho = -0.210116 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 48 nu = 0.431215 obj = -0.575202, rho = -0.157248 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 140 nu = 0.347884 obj = -0.678071, rho = -0.171279 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 98 nu = 0.292657 obj = -0.803775, rho = -0.220241 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 62 nu = 0.238565 obj = -0.948697, rho = -0.197114 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.193976 obj = -1.114484, rho = -0.229300 nSV = 24, nBSV = 13 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 160 nu = 0.157342 obj = -1.328207, rho = -0.215321 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.129480 obj = -1.586592, rho = -0.207718 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 81 nu = 0.106948 obj = -1.914502, rho = -0.174460 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.*.* optimization finished, #iter = 157 nu = 0.091469 obj = -2.297972, rho = -0.208236 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 178 nu = 0.079452 obj = -2.712645, rho = -0.303820 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 295 nu = 0.062484 obj = -3.157654, rho = -0.325755 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.051746 obj = -3.710072, rho = -0.361909 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.043715 obj = -4.269229, rho = -0.278487 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 93 nu = 0.037593 obj = -4.633551, rho = -0.212424 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 121 nu = 0.027681 obj = -4.650483, rho = -0.215587 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 121 nu = 0.019244 obj = -4.650483, rho = -0.215587 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 121 nu = 0.013378 obj = -4.650483, rho = -0.215587 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 121 nu = 0.009300 obj = -4.650483, rho = -0.215587 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 37 nu = 0.598646 obj = -0.395688, rho = -0.028464 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 52 nu = 0.496987 obj = -0.472487, rho = -0.008431 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 45 nu = 0.423864 obj = -0.557487, rho = -0.128685 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 41 nu = 0.344642 obj = -0.647551, rho = -0.175684 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 81 nu = 0.286049 obj = -0.744280, rho = -0.128561 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 73 nu = 0.224145 obj = -0.855508, rho = -0.123222 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 182 nu = 0.182278 obj = -0.980236, rho = -0.094066 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 83 nu = 0.142177 obj = -1.117388, rho = -0.144816 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 54 nu = 0.113799 obj = -1.284239, rho = -0.254412 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 215 nu = 0.092620 obj = -1.454114, rho = -0.255127 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) ..*.* optimization finished, #iter = 300 nu = 0.072560 obj = -1.632873, rho = -0.265568 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 271 nu = 0.055133 obj = -1.852492, rho = -0.253132 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 180 nu = 0.043808 obj = -2.138899, rho = -0.211635 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 211 nu = 0.036890 obj = -2.440446, rho = -0.109910 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..* optimization finished, #iter = 287 nu = 0.031702 obj = -2.587801, rho = 0.007957 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ...*.* optimization finished, #iter = 457 nu = 0.022158 obj = -2.587963, rho = 0.005844 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ...*.* optimization finished, #iter = 457 nu = 0.015404 obj = -2.587963, rho = 0.005844 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ...*.* optimization finished, #iter = 457 nu = 0.010709 obj = -2.587963, rho = 0.005844 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ...*.* optimization finished, #iter = 457 nu = 0.007445 obj = -2.587963, rho = 0.005844 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ...*.* optimization finished, #iter = 457 nu = 0.005176 obj = -2.587963, rho = 0.005844 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 52 nu = 0.605657 obj = -0.405504, rho = -0.043909 nSV = 64, nBSV = 58 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 37 nu = 0.511924 obj = -0.486492, rho = 0.026683 nSV = 54, nBSV = 47 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 29 nu = 0.421644 obj = -0.583292, rho = -0.018409 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 87 nu = 0.348920 obj = -0.699720, rho = -0.080809 nSV = 38, nBSV = 30 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.291849 obj = -0.846827, rho = -0.104192 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 75 nu = 0.246618 obj = -1.023142, rho = -0.081692 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 53 nu = 0.207648 obj = -1.242445, rho = 0.020360 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 72 nu = 0.172767 obj = -1.504423, rho = 0.048231 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 94 nu = 0.149754 obj = -1.818818, rho = -0.112365 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 64 nu = 0.124025 obj = -2.179743, rho = -0.168594 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 85 nu = 0.106862 obj = -2.566119, rho = -0.428999 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 85 nu = 0.086764 obj = -2.985097, rho = -0.442254 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) .*.* optimization finished, #iter = 244 nu = 0.068747 obj = -3.501530, rho = -0.314500 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) *...* optimization finished, #iter = 343 nu = 0.055315 obj = -4.149595, rho = -0.321869 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.045155 obj = -5.010108, rho = -0.437959 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 86 nu = 0.039670 obj = -6.032775, rho = -0.962237 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 155 nu = 0.035521 obj = -6.957657, rho = -1.540230 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .*..* optimization finished, #iter = 313 nu = 0.030443 obj = -7.356358, rho = -2.079637 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) .*..* optimization finished, #iter = 313 nu = 0.021164 obj = -7.356358, rho = -2.079637 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) .*..* optimization finished, #iter = 313 nu = 0.014713 obj = -7.356358, rho = -2.079637 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 40 nu = 0.634823 obj = -0.430783, rho = 0.027043 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 60 nu = 0.544779 obj = -0.514931, rho = 0.091152 nSV = 58, nBSV = 51 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 58 nu = 0.454027 obj = -0.611540, rho = 0.055925 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 62 nu = 0.377925 obj = -0.721460, rho = 0.087325 nSV = 42, nBSV = 34 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 86 nu = 0.307138 obj = -0.846014, rho = 0.115953 nSV = 37, nBSV = 27 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 110 nu = 0.251527 obj = -0.989763, rho = 0.149280 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 94 nu = 0.205784 obj = -1.158368, rho = 0.139229 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..* optimization finished, #iter = 298 nu = 0.163047 obj = -1.358931, rho = 0.156211 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.131331 obj = -1.630692, rho = 0.172711 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 174 nu = 0.116792 obj = -1.937016, rho = 0.391495 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 146 nu = 0.100000 obj = -2.220351, rho = 0.563598 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) ..**.* optimization finished, #iter = 350 nu = 0.080983 obj = -2.381944, rho = 0.822437 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) .*.* optimization finished, #iter = 257 nu = 0.062463 obj = -2.476574, rho = 1.048476 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .*...* optimization finished, #iter = 448 nu = 0.043988 obj = -2.482966, rho = 1.074039 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .*...* optimization finished, #iter = 448 nu = 0.030580 obj = -2.482966, rho = 1.074039 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .*...* optimization finished, #iter = 448 nu = 0.021259 obj = -2.482966, rho = 1.074039 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .*...* optimization finished, #iter = 448 nu = 0.014779 obj = -2.482966, rho = 1.074039 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .*...* optimization finished, #iter = 448 nu = 0.010274 obj = -2.482966, rho = 1.074039 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .*...* optimization finished, #iter = 448 nu = 0.007143 obj = -2.482966, rho = 1.074039 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .*...* optimization finished, #iter = 448 nu = 0.004965 obj = -2.482966, rho = 1.074039 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 40 nu = 0.550930 obj = -0.363987, rho = -0.204786 nSV = 57, nBSV = 54 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 26 nu = 0.460000 obj = -0.434490, rho = -0.170802 nSV = 48, nBSV = 45 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 39 nu = 0.378277 obj = -0.515312, rho = -0.175537 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 68 nu = 0.318940 obj = -0.610138, rho = -0.156556 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *..* optimization finished, #iter = 248 nu = 0.263641 obj = -0.712949, rho = -0.057575 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 241 nu = 0.210407 obj = -0.836497, rho = -0.065734 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 93 nu = 0.171553 obj = -0.992127, rho = -0.068383 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 270 nu = 0.141401 obj = -1.178664, rho = -0.076546 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 137 nu = 0.116725 obj = -1.404627, rho = -0.076582 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 247 nu = 0.095207 obj = -1.674820, rho = -0.186625 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.078139 obj = -2.036857, rho = -0.249054 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*...* optimization finished, #iter = 429 nu = 0.068382 obj = -2.438642, rho = -0.411642 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 93 nu = 0.059403 obj = -2.867786, rho = -0.655274 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 195 nu = 0.049186 obj = -3.196276, rho = -0.809197 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 144 nu = 0.041667 obj = -3.398070, rho = -0.718138 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 141 nu = 0.029098 obj = -3.398177, rho = -0.715695 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 141 nu = 0.020229 obj = -3.398177, rho = -0.715695 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 141 nu = 0.014063 obj = -3.398177, rho = -0.715695 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 141 nu = 0.009777 obj = -3.398177, rho = -0.715695 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 141 nu = 0.006797 obj = -3.398177, rho = -0.715695 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 41 nu = 0.583163 obj = -0.391556, rho = -0.055452 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 44 nu = 0.490054 obj = -0.471931, rho = -0.113829 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 36 nu = 0.404971 obj = -0.569897, rho = -0.135519 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 35 nu = 0.344824 obj = -0.689506, rho = -0.098905 nSV = 37, nBSV = 33 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.290098 obj = -0.825121, rho = -0.144485 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 72 nu = 0.242121 obj = -0.988723, rho = -0.174158 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 68 nu = 0.198535 obj = -1.192796, rho = -0.247341 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 33 nu = 0.163327 obj = -1.452674, rho = -0.309485 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 62 nu = 0.143106 obj = -1.765984, rho = -0.252840 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 79 nu = 0.121609 obj = -2.118753, rho = -0.218798 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.102122 obj = -2.517950, rho = -0.351305 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 91 nu = 0.084823 obj = -2.959920, rho = -0.494409 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 145 nu = 0.067687 obj = -3.481902, rho = -0.411270 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 149 nu = 0.053706 obj = -4.195209, rho = -0.346828 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 130 nu = 0.044757 obj = -5.180346, rho = -0.138069 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 135 nu = 0.039110 obj = -6.418791, rho = 0.018146 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 62 nu = 0.034981 obj = -7.833177, rho = 0.142444 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.032658 obj = -8.952595, rho = 0.127111 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.026516 obj = -9.219510, rho = 0.060163 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.018434 obj = -9.219510, rho = 0.060163 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 36 nu = 0.538978 obj = -0.359331, rho = -0.138370 nSV = 55, nBSV = 52 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 51 nu = 0.445010 obj = -0.432816, rho = -0.140752 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.374337 obj = -0.521443, rho = -0.166139 nSV = 41, nBSV = 33 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.315575 obj = -0.630472, rho = -0.168183 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 31 nu = 0.264497 obj = -0.755749, rho = -0.166769 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 79 nu = 0.227784 obj = -0.888000, rho = -0.105573 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 175 nu = 0.187723 obj = -1.027319, rho = -0.098832 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *..* optimization finished, #iter = 243 nu = 0.145956 obj = -1.196009, rho = -0.126961 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 138 nu = 0.123306 obj = -1.392104, rho = -0.291964 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 180 nu = 0.095481 obj = -1.609587, rho = -0.325455 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.083921 obj = -1.846616, rho = -0.473823 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *....* optimization finished, #iter = 442 nu = 0.066588 obj = -2.037280, rho = -0.551536 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 130 nu = 0.051770 obj = -2.178970, rho = -0.691328 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ......*....* optimization finished, #iter = 1006 nu = 0.039407 obj = -2.224551, rho = -0.777011 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ......*....* optimization finished, #iter = 1006 nu = 0.027396 obj = -2.224551, rho = -0.777011 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ......*....* optimization finished, #iter = 1006 nu = 0.019045 obj = -2.224551, rho = -0.777011 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ......*....* optimization finished, #iter = 1006 nu = 0.013240 obj = -2.224551, rho = -0.777011 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ......*....* optimization finished, #iter = 1006 nu = 0.009204 obj = -2.224551, rho = -0.777011 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ......*....* optimization finished, #iter = 1006 nu = 0.006399 obj = -2.224551, rho = -0.777011 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ......*....* optimization finished, #iter = 1006 nu = 0.004448 obj = -2.224551, rho = -0.777011 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 50 nu = 0.566292 obj = -0.381695, rho = -0.114758 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 43 nu = 0.477333 obj = -0.457553, rho = -0.095659 nSV = 53, nBSV = 44 Total nSV = 53 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 52 nu = 0.400237 obj = -0.547815, rho = -0.089241 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 56 nu = 0.329327 obj = -0.657795, rho = -0.093145 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.276184 obj = -0.789914, rho = -0.110513 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 67 nu = 0.229593 obj = -0.952948, rho = -0.110121 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 70 nu = 0.197960 obj = -1.133332, rho = -0.306534 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 185 nu = 0.157286 obj = -1.346560, rho = -0.310752 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 98 nu = 0.130052 obj = -1.632416, rho = -0.294884 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) *.* optimization finished, #iter = 136 nu = 0.110763 obj = -1.966718, rho = -0.320171 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 58 nu = 0.090867 obj = -2.394214, rho = -0.374193 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 73 nu = 0.079765 obj = -2.898355, rho = -0.791991 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) *.* optimization finished, #iter = 153 nu = 0.067241 obj = -3.474347, rho = -0.936327 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 95.5% (955/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.056512 obj = -4.101961, rho = -1.137650 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96% (960/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.047791 obj = -4.803538, rho = -1.608697 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .*..* optimization finished, #iter = 301 nu = 0.041752 obj = -5.354877, rho = -2.114220 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.4% (954/1000) (classification) ..*.* optimization finished, #iter = 359 nu = 0.032290 obj = -5.424942, rho = -2.370075 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) ..*.* optimization finished, #iter = 359 nu = 0.022448 obj = -5.424942, rho = -2.370075 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) ..*.* optimization finished, #iter = 359 nu = 0.015606 obj = -5.424942, rho = -2.370075 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) ..*.* optimization finished, #iter = 359 nu = 0.010849 obj = -5.424942, rho = -2.370075 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 62 nu = 0.651598 obj = -0.436497, rho = -0.241312 nSV = 68, nBSV = 61 Total nSV = 68 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 69 nu = 0.543073 obj = -0.526384, rho = -0.294832 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 51 nu = 0.453558 obj = -0.636176, rho = -0.250875 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 47 nu = 0.380615 obj = -0.768024, rho = -0.253719 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 48 nu = 0.323487 obj = -0.927162, rho = -0.361499 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 26 nu = 0.273562 obj = -1.102805, rho = -0.457766 nSV = 30, nBSV = 26 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 44 nu = 0.232183 obj = -1.292637, rho = -0.385937 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 82 nu = 0.191399 obj = -1.490389, rho = -0.387634 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 223 nu = 0.156201 obj = -1.687929, rho = -0.423321 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .....* optimization finished, #iter = 554 nu = 0.120054 obj = -1.890353, rho = -0.428203 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..*......* optimization finished, #iter = 838 nu = 0.091651 obj = -2.146728, rho = -0.420292 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 128 nu = 0.075077 obj = -2.451244, rho = -0.534180 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.060253 obj = -2.727364, rho = -0.700567 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 258 nu = 0.048783 obj = -2.896868, rho = -0.724898 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 199 nu = 0.036419 obj = -2.956998, rho = -0.732247 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 199 nu = 0.025318 obj = -2.956998, rho = -0.732247 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 199 nu = 0.017601 obj = -2.956998, rho = -0.732247 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 199 nu = 0.012236 obj = -2.956998, rho = -0.732247 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 199 nu = 0.008507 obj = -2.956998, rho = -0.732247 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 199 nu = 0.005914 obj = -2.956998, rho = -0.732247 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 41 nu = 0.553423 obj = -0.363050, rho = -0.203758 nSV = 56, nBSV = 53 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 62 nu = 0.465331 obj = -0.429438, rho = -0.216850 nSV = 48, nBSV = 40 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 52 nu = 0.377742 obj = -0.507373, rho = -0.206336 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 32 nu = 0.315233 obj = -0.597913, rho = -0.143202 nSV = 33, nBSV = 30 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 23 nu = 0.259787 obj = -0.696470, rho = -0.192545 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 45 nu = 0.209254 obj = -0.804756, rho = -0.188489 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 74 nu = 0.166386 obj = -0.929046, rho = -0.168794 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 60 nu = 0.140756 obj = -1.054359, rho = -0.224722 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 88 nu = 0.114029 obj = -1.162033, rho = -0.238712 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 61 nu = 0.085345 obj = -1.240439, rho = -0.285940 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 57 nu = 0.063730 obj = -1.336189, rho = -0.301737 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 156 nu = 0.050134 obj = -1.367660, rho = -0.314860 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.034851 obj = -1.367660, rho = -0.314775 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.024228 obj = -1.367660, rho = -0.314775 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.016843 obj = -1.367660, rho = -0.314775 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.011709 obj = -1.367660, rho = -0.314775 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.008140 obj = -1.367660, rho = -0.314775 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.005659 obj = -1.367660, rho = -0.314775 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.003934 obj = -1.367660, rho = -0.314775 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.002735 obj = -1.367660, rho = -0.314775 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 45 nu = 0.560865 obj = -0.386331, rho = -0.206652 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 54 nu = 0.479153 obj = -0.468394, rho = -0.154207 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 57 nu = 0.396844 obj = -0.568144, rho = -0.156626 nSV = 45, nBSV = 37 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.340108 obj = -0.694423, rho = -0.112515 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 35 nu = 0.290899 obj = -0.840973, rho = -0.106255 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 44 nu = 0.245091 obj = -1.017733, rho = -0.089993 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 62 nu = 0.209992 obj = -1.222379, rho = -0.176823 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 161 nu = 0.175386 obj = -1.442258, rho = -0.220559 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.142909 obj = -1.699656, rho = -0.160072 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 176 nu = 0.118865 obj = -1.992000, rho = -0.068086 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 48 nu = 0.098754 obj = -2.338581, rho = -0.085290 nSV = 12, nBSV = 7 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 138 nu = 0.087357 obj = -2.574132, rho = -0.253505 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *...* optimization finished, #iter = 312 nu = 0.065010 obj = -2.669073, rho = -0.145933 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ...* optimization finished, #iter = 367 nu = 0.046668 obj = -2.768070, rho = -0.089159 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 252 nu = 0.034747 obj = -2.820955, rho = 0.056471 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 252 nu = 0.024156 obj = -2.820955, rho = 0.056471 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 252 nu = 0.016793 obj = -2.820955, rho = 0.056471 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 252 nu = 0.011674 obj = -2.820955, rho = 0.056471 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 252 nu = 0.008116 obj = -2.820955, rho = 0.056471 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 252 nu = 0.005642 obj = -2.820955, rho = 0.056471 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 51 nu = 0.625758 obj = -0.407758, rho = 0.117114 nSV = 65, nBSV = 60 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.518486 obj = -0.481039, rho = 0.109154 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 50 nu = 0.421144 obj = -0.568339, rho = 0.074951 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 60 nu = 0.348209 obj = -0.674163, rho = 0.150335 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 67 nu = 0.278165 obj = -0.802155, rho = 0.141631 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 57 nu = 0.233253 obj = -0.966951, rho = 0.084959 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.196806 obj = -1.171522, rho = 0.083727 nSV = 21, nBSV = 17 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 83 nu = 0.163297 obj = -1.408586, rho = 0.029434 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 38 nu = 0.138157 obj = -1.707962, rho = 0.114108 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 95 nu = 0.117366 obj = -2.040616, rho = 0.280485 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.095038 obj = -2.456440, rho = 0.352739 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.081362 obj = -2.972253, rho = 0.433734 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) .*.* optimization finished, #iter = 278 nu = 0.065531 obj = -3.617268, rho = 0.479816 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) .*.* optimization finished, #iter = 270 nu = 0.053776 obj = -4.531933, rho = 0.423996 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) .*.......* optimization finished, #iter = 831 nu = 0.045502 obj = -5.841552, rho = 0.397234 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) ..*.* optimization finished, #iter = 356 nu = 0.040298 obj = -7.716889, rho = 0.450761 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96% (960/1000) (classification) .*...........* optimization finished, #iter = 1265 nu = 0.037660 obj = -10.207765, rho = 0.559203 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 95.2% (952/1000) (classification) ..*.* optimization finished, #iter = 314 nu = 0.035814 obj = -13.350564, rho = 0.727799 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 95.3% (953/1000) (classification) .*.* optimization finished, #iter = 228 nu = 0.032605 obj = -17.011480, rho = 0.893405 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 94.5% (945/1000) (classification) .*.* optimization finished, #iter = 263 nu = 0.028579 obj = -21.706584, rho = 1.235971 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 93.2% (932/1000) (classification) * optimization finished, #iter = 52 nu = 0.607313 obj = -0.404037, rho = -0.113556 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 31 nu = 0.508460 obj = -0.485514, rho = -0.101954 nSV = 53, nBSV = 50 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 39 nu = 0.430160 obj = -0.578769, rho = -0.155607 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 56 nu = 0.348573 obj = -0.688000, rho = -0.208542 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 81 nu = 0.301763 obj = -0.809061, rho = -0.289907 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 46 nu = 0.239559 obj = -0.943954, rho = -0.280931 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.199516 obj = -1.097313, rho = -0.485149 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 57 nu = 0.158548 obj = -1.270235, rho = -0.567265 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.135178 obj = -1.446029, rho = -0.651081 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) *..* optimization finished, #iter = 293 nu = 0.105875 obj = -1.584050, rho = -0.630296 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ..*.* optimization finished, #iter = 362 nu = 0.079357 obj = -1.736718, rho = -0.607450 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) .*.* optimization finished, #iter = 238 nu = 0.060260 obj = -1.928386, rho = -0.643089 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 194 nu = 0.048048 obj = -2.124904, rho = -0.876144 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.036772 obj = -2.287097, rho = -1.114211 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) ..* optimization finished, #iter = 219 nu = 0.029134 obj = -2.365679, rho = -1.405346 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) ..* optimization finished, #iter = 219 nu = 0.020253 obj = -2.365679, rho = -1.405346 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) ..* optimization finished, #iter = 219 nu = 0.014080 obj = -2.365679, rho = -1.405346 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) ..* optimization finished, #iter = 219 nu = 0.009788 obj = -2.365679, rho = -1.405346 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) ..* optimization finished, #iter = 219 nu = 0.006805 obj = -2.365679, rho = -1.405346 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) ..* optimization finished, #iter = 219 nu = 0.004731 obj = -2.365679, rho = -1.405346 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 30 nu = 0.554265 obj = -0.382083, rho = 0.162590 nSV = 56, nBSV = 53 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 94.6% (946/1000) (classification) * optimization finished, #iter = 36 nu = 0.472059 obj = -0.464388, rho = 0.271607 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 93.9% (939/1000) (classification) * optimization finished, #iter = 46 nu = 0.398655 obj = -0.562559, rho = 0.233925 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 39 nu = 0.343705 obj = -0.677516, rho = 0.176573 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 42 nu = 0.285289 obj = -0.811770, rho = 0.223812 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 66 nu = 0.240437 obj = -0.965541, rho = 0.300297 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 76 nu = 0.195943 obj = -1.141615, rho = 0.392505 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 95.4% (954/1000) (classification) .* optimization finished, #iter = 174 nu = 0.162929 obj = -1.347652, rho = 0.422306 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 95.7% (957/1000) (classification) *..* optimization finished, #iter = 212 nu = 0.134766 obj = -1.596692, rho = 0.531028 nSV = 21, nBSV = 10 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 92 nu = 0.110045 obj = -1.884739, rho = 0.556214 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 56 nu = 0.095331 obj = -2.185429, rho = 0.532104 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 196 nu = 0.080925 obj = -2.394147, rho = 0.159733 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ...*........* optimization finished, #iter = 1184 nu = 0.061441 obj = -2.488717, rho = -0.044907 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ....*..* optimization finished, #iter = 650 nu = 0.044338 obj = -2.502750, rho = -0.143768 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ....*..* optimization finished, #iter = 650 nu = 0.030824 obj = -2.502750, rho = -0.143768 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ....*..* optimization finished, #iter = 650 nu = 0.021428 obj = -2.502750, rho = -0.143768 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ....*..* optimization finished, #iter = 650 nu = 0.014897 obj = -2.502750, rho = -0.143768 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ....*..* optimization finished, #iter = 650 nu = 0.010356 obj = -2.502750, rho = -0.143768 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ....*..* optimization finished, #iter = 650 nu = 0.007200 obj = -2.502750, rho = -0.143768 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ....*..* optimization finished, #iter = 650 nu = 0.005005 obj = -2.502750, rho = -0.143768 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 44 nu = 0.568094 obj = -0.394229, rho = -0.132107 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 97% (97/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 31 nu = 0.498624 obj = -0.481625, rho = -0.093607 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 73 nu = 0.411124 obj = -0.581993, rho = -0.085050 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 96% (96/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 64 nu = 0.342415 obj = -0.712863, rho = -0.126276 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 45 nu = 0.292631 obj = -0.881256, rho = -0.174611 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 64 nu = 0.258714 obj = -1.075438, rho = -0.269076 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 41 nu = 0.217718 obj = -1.288230, rho = -0.191939 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 41 nu = 0.183955 obj = -1.534773, rho = -0.288380 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 82 nu = 0.150312 obj = -1.835212, rho = -0.269641 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 53 nu = 0.123120 obj = -2.217667, rho = -0.332355 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 65 nu = 0.103634 obj = -2.680693, rho = -0.607604 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 82 nu = 0.093086 obj = -3.184253, rho = -0.947109 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.080535 obj = -3.546609, rho = -1.199136 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 148 nu = 0.061090 obj = -3.841770, rho = -1.256413 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 178 nu = 0.047924 obj = -4.005829, rho = -1.406960 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..*..* optimization finished, #iter = 403 nu = 0.034648 obj = -4.046717, rho = -1.386390 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*..* optimization finished, #iter = 403 nu = 0.024087 obj = -4.046717, rho = -1.386390 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*..* optimization finished, #iter = 403 nu = 0.016745 obj = -4.046717, rho = -1.386390 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*..* optimization finished, #iter = 403 nu = 0.011641 obj = -4.046717, rho = -1.386390 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*..* optimization finished, #iter = 403 nu = 0.008093 obj = -4.046717, rho = -1.386390 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.609218 obj = -0.422584, rho = -0.067662 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 41 nu = 0.523085 obj = -0.517273, rho = -0.109968 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 31 nu = 0.454800 obj = -0.629620, rho = -0.132517 nSV = 46, nBSV = 43 Total nSV = 46 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 44 nu = 0.381942 obj = -0.756849, rho = -0.173350 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 95 nu = 0.317460 obj = -0.906681, rho = -0.154977 nSV = 36, nBSV = 25 Total nSV = 36 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 164 nu = 0.259580 obj = -1.094214, rho = -0.120674 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 83 nu = 0.217181 obj = -1.336083, rho = -0.090451 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 67 nu = 0.188038 obj = -1.615162, rho = -0.090483 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 87 nu = 0.161236 obj = -1.937353, rho = -0.139912 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*..* optimization finished, #iter = 350 nu = 0.130319 obj = -2.322601, rho = -0.153193 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 176 nu = 0.109493 obj = -2.794183, rho = -0.157890 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 178 nu = 0.090884 obj = -3.375624, rho = -0.098452 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*..* optimization finished, #iter = 365 nu = 0.074639 obj = -4.139330, rho = -0.088455 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 285 nu = 0.068375 obj = -5.026840, rho = -0.250838 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 141 nu = 0.059194 obj = -5.888653, rho = -0.207941 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ...*....*.* optimization finished, #iter = 734 nu = 0.051367 obj = -6.441632, rho = -0.153021 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) ............*.......* optimization finished, #iter = 1917 nu = 0.039525 obj = -6.640475, rho = -0.018814 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) ............*.......* optimization finished, #iter = 1917 nu = 0.027478 obj = -6.640475, rho = -0.018814 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) ............*.......* optimization finished, #iter = 1917 nu = 0.019102 obj = -6.640475, rho = -0.018814 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) ............*.......* optimization finished, #iter = 1917 nu = 0.013280 obj = -6.640475, rho = -0.018814 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 36 nu = 0.600151 obj = -0.396616, rho = -0.199145 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 74 nu = 0.504668 obj = -0.469730, rho = -0.257968 nSV = 53, nBSV = 45 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 41 nu = 0.408374 obj = -0.558283, rho = -0.229798 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 63 nu = 0.340690 obj = -0.664142, rho = -0.192925 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 48 nu = 0.279814 obj = -0.794401, rho = -0.215144 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 67 nu = 0.233483 obj = -0.944426, rho = -0.221741 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 98 nu = 0.189770 obj = -1.124844, rho = -0.181805 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 53 nu = 0.156553 obj = -1.359105, rho = -0.183505 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 46 nu = 0.137818 obj = -1.631437, rho = -0.339011 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 71 nu = 0.116249 obj = -1.913488, rho = -0.562404 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 74 nu = 0.098975 obj = -2.163948, rho = -0.724658 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*.* optimization finished, #iter = 331 nu = 0.075314 obj = -2.401007, rho = -0.758531 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 206 nu = 0.057755 obj = -2.672360, rho = -0.706243 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..* optimization finished, #iter = 233 nu = 0.045369 obj = -2.974999, rho = -0.628965 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*........* optimization finished, #iter = 987 nu = 0.036525 obj = -3.231402, rho = -0.519611 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..*..* optimization finished, #iter = 464 nu = 0.028260 obj = -3.300466, rho = -0.486374 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..*..* optimization finished, #iter = 464 nu = 0.019646 obj = -3.300466, rho = -0.486374 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..*..* optimization finished, #iter = 464 nu = 0.013658 obj = -3.300466, rho = -0.486374 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..*..* optimization finished, #iter = 464 nu = 0.009495 obj = -3.300466, rho = -0.486374 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..*..* optimization finished, #iter = 464 nu = 0.006601 obj = -3.300466, rho = -0.486374 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 48 nu = 0.576129 obj = -0.412096, rho = -0.224817 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 36 nu = 0.501939 obj = -0.512673, rho = -0.161823 nSV = 52, nBSV = 49 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 42 nu = 0.444376 obj = -0.628008, rho = -0.073951 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 28 nu = 0.370666 obj = -0.766088, rho = -0.036468 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 33 nu = 0.319734 obj = -0.928431, rho = -0.056200 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 46 nu = 0.273633 obj = -1.123105, rho = -0.144010 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 39 nu = 0.236063 obj = -1.347047, rho = -0.131188 nSV = 25, nBSV = 21 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.195396 obj = -1.568522, rho = -0.143766 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 184 nu = 0.157305 obj = -1.826295, rho = -0.226709 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *.....* optimization finished, #iter = 593 nu = 0.129744 obj = -2.112124, rho = -0.367044 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) .*.....* optimization finished, #iter = 630 nu = 0.102534 obj = -2.417526, rho = -0.426577 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.081473 obj = -2.803608, rho = -0.408181 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 137 nu = 0.067292 obj = -3.246757, rho = -0.296808 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) ..* optimization finished, #iter = 297 nu = 0.057435 obj = -3.628383, rho = -0.099091 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) ..*.* optimization finished, #iter = 354 nu = 0.044080 obj = -3.893110, rho = -0.057570 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) ..*.* optimization finished, #iter = 324 nu = 0.033880 obj = -4.070233, rho = -0.398319 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) ..*.* optimization finished, #iter = 307 nu = 0.024275 obj = -4.078967, rho = -0.401839 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*.* optimization finished, #iter = 307 nu = 0.016876 obj = -4.078967, rho = -0.401839 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*.* optimization finished, #iter = 307 nu = 0.011732 obj = -4.078967, rho = -0.401839 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*.* optimization finished, #iter = 307 nu = 0.008156 obj = -4.078967, rho = -0.401839 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 41 nu = 0.600866 obj = -0.412104, rho = -0.034511 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 37 nu = 0.511690 obj = -0.501099, rho = 0.023548 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 32 nu = 0.434635 obj = -0.608304, rho = 0.117299 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 52 nu = 0.370758 obj = -0.727716, rho = 0.196824 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 54 nu = 0.306553 obj = -0.869946, rho = 0.156269 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 85 nu = 0.253825 obj = -1.040293, rho = 0.110573 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 60 nu = 0.214792 obj = -1.243189, rho = 0.145258 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 136 nu = 0.183857 obj = -1.449963, rho = 0.346841 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 90 nu = 0.143611 obj = -1.690152, rho = 0.380385 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 129 nu = 0.116570 obj = -1.975060, rho = 0.355531 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 96 nu = 0.096969 obj = -2.326008, rho = 0.352494 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 179 nu = 0.087299 obj = -2.580927, rho = -0.008457 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ...*..* optimization finished, #iter = 556 nu = 0.067361 obj = -2.647999, rho = -0.144073 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ....*.* optimization finished, #iter = 573 nu = 0.046925 obj = -2.651976, rho = -0.156664 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ....*..* optimization finished, #iter = 678 nu = 0.032663 obj = -2.652056, rho = -0.170199 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ....*..* optimization finished, #iter = 678 nu = 0.022707 obj = -2.652056, rho = -0.170199 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ....*..* optimization finished, #iter = 678 nu = 0.015786 obj = -2.652056, rho = -0.170199 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ....*..* optimization finished, #iter = 678 nu = 0.010974 obj = -2.652056, rho = -0.170199 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ....*..* optimization finished, #iter = 678 nu = 0.007629 obj = -2.652056, rho = -0.170199 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ....*..* optimization finished, #iter = 678 nu = 0.005304 obj = -2.652056, rho = -0.170199 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 57 nu = 0.526715 obj = -0.346732, rho = -0.218891 nSV = 56, nBSV = 50 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 53 nu = 0.438716 obj = -0.412487, rho = -0.210928 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 59 nu = 0.369417 obj = -0.484235, rho = -0.182211 nSV = 38, nBSV = 34 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 43 nu = 0.301424 obj = -0.563114, rho = -0.187899 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 91 nu = 0.246247 obj = -0.647418, rho = -0.172488 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 44 nu = 0.194688 obj = -0.742829, rho = -0.122516 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 40 nu = 0.155719 obj = -0.855108, rho = -0.172944 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 96 nu = 0.128137 obj = -0.971276, rho = -0.164155 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 90 nu = 0.099663 obj = -1.094247, rho = -0.156934 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *..* optimization finished, #iter = 275 nu = 0.076375 obj = -1.237870, rho = -0.165275 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 163 nu = 0.058678 obj = -1.440287, rho = -0.159197 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 155 nu = 0.050049 obj = -1.684136, rho = -0.023565 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 44 nu = 0.042416 obj = -1.918468, rho = -0.295460 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.035437 obj = -2.000346, rho = -0.608835 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.024635 obj = -2.000346, rho = -0.608835 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.017126 obj = -2.000346, rho = -0.608835 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.011906 obj = -2.000346, rho = -0.608835 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.008277 obj = -2.000346, rho = -0.608835 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.005754 obj = -2.000346, rho = -0.608835 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.004000 obj = -2.000346, rho = -0.608835 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 40 nu = 0.562035 obj = -0.379576, rho = -0.243606 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 32 nu = 0.472239 obj = -0.457651, rho = -0.305310 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 0.395984 obj = -0.549880, rho = -0.309892 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 54 nu = 0.332139 obj = -0.662067, rho = -0.319246 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 90 nu = 0.281963 obj = -0.793231, rho = -0.310976 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.234359 obj = -0.944775, rho = -0.307920 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.191964 obj = -1.122992, rho = -0.355744 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 137 nu = 0.158206 obj = -1.332611, rho = -0.368394 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*.* optimization finished, #iter = 323 nu = 0.128376 obj = -1.608230, rho = -0.364307 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 232 nu = 0.108247 obj = -1.951630, rho = -0.224496 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 91 nu = 0.090001 obj = -2.385905, rho = -0.210757 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 211 nu = 0.075772 obj = -2.943621, rho = -0.186912 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 97 nu = 0.064895 obj = -3.672880, rho = -0.157989 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 149 nu = 0.055899 obj = -4.605941, rho = -0.291741 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.049713 obj = -5.778115, rho = -0.465499 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 99 nu = 0.044631 obj = -7.077030, rho = -0.629946 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 131 nu = 0.039423 obj = -8.510594, rho = -0.295701 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 147 nu = 0.035996 obj = -9.706996, rho = -0.397316 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 220 nu = 0.028373 obj = -9.862439, rho = -0.486877 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 220 nu = 0.019724 obj = -9.862439, rho = -0.486877 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 40 nu = 0.573414 obj = -0.381352, rho = -0.048900 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 44 nu = 0.477505 obj = -0.455802, rho = 0.031279 nSV = 52, nBSV = 45 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 35 nu = 0.397085 obj = -0.545496, rho = 0.058365 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 51 nu = 0.337682 obj = -0.648256, rho = 0.044917 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 47 nu = 0.279437 obj = -0.761108, rho = 0.075513 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 63 nu = 0.227268 obj = -0.888173, rho = 0.101217 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 79 nu = 0.191431 obj = -1.011321, rho = -0.011244 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 62 nu = 0.152725 obj = -1.129457, rho = 0.003252 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.121957 obj = -1.227903, rho = -0.028839 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 145 nu = 0.091549 obj = -1.290421, rho = 0.012678 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ..*.* optimization finished, #iter = 311 nu = 0.067937 obj = -1.332339, rho = 0.006837 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*...* optimization finished, #iter = 402 nu = 0.048982 obj = -1.348869, rho = -0.015062 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.034525 obj = -1.354686, rho = -0.002037 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.024001 obj = -1.354686, rho = -0.002037 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.016686 obj = -1.354686, rho = -0.002037 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.011600 obj = -1.354686, rho = -0.002037 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.008064 obj = -1.354686, rho = -0.002037 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.005606 obj = -1.354686, rho = -0.002037 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.003897 obj = -1.354686, rho = -0.002037 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.002709 obj = -1.354686, rho = -0.002037 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 38 nu = 0.594470 obj = -0.407468, rho = -0.101781 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 39 nu = 0.501928 obj = -0.498318, rho = -0.115355 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 46 nu = 0.428771 obj = -0.609848, rho = -0.098731 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 29 nu = 0.379743 obj = -0.737838, rho = -0.163570 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.312679 obj = -0.872544, rho = -0.126469 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 52 nu = 0.257153 obj = -1.039949, rho = -0.201463 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 76 nu = 0.210868 obj = -1.238556, rho = -0.173589 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 62 nu = 0.172078 obj = -1.484966, rho = -0.194924 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 267 nu = 0.143989 obj = -1.798701, rho = -0.169567 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *..* optimization finished, #iter = 269 nu = 0.119305 obj = -2.191754, rho = -0.123403 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 63 nu = 0.100428 obj = -2.710727, rho = -0.150724 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.089683 obj = -3.342097, rho = -0.195304 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 80 nu = 0.080636 obj = -3.961358, rho = -0.297570 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 86 nu = 0.069486 obj = -4.465427, rho = -0.445774 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *..* optimization finished, #iter = 230 nu = 0.053640 obj = -4.790346, rho = -0.654343 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 78 nu = 0.041415 obj = -5.101814, rho = -0.812384 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 84 nu = 0.030531 obj = -5.129127, rho = -0.887838 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 84 nu = 0.021225 obj = -5.129127, rho = -0.887838 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 84 nu = 0.014755 obj = -5.129127, rho = -0.887838 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 84 nu = 0.010258 obj = -5.129127, rho = -0.887838 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 40 nu = 0.512257 obj = -0.348175, rho = -0.138531 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 34 nu = 0.439188 obj = -0.417046, rho = -0.083291 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 59 nu = 0.373268 obj = -0.486238, rho = 0.036055 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 38 nu = 0.303266 obj = -0.563066, rho = 0.014957 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.247136 obj = -0.640016, rho = 0.083462 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *...* optimization finished, #iter = 324 nu = 0.191644 obj = -0.726415, rho = 0.115633 nSV = 25, nBSV = 14 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 63 nu = 0.153511 obj = -0.833536, rho = 0.105195 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 81 nu = 0.125221 obj = -0.956650, rho = 0.221241 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 86 nu = 0.099672 obj = -1.065066, rho = 0.304493 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.075020 obj = -1.197118, rho = 0.346486 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 88 nu = 0.062819 obj = -1.326895, rho = 0.460333 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *...* optimization finished, #iter = 375 nu = 0.048299 obj = -1.403754, rho = 0.458105 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.036248 obj = -1.441276, rho = 0.525945 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 173 nu = 0.025561 obj = -1.442637, rho = 0.554009 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 173 nu = 0.017770 obj = -1.442637, rho = 0.554009 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 173 nu = 0.012354 obj = -1.442637, rho = 0.554009 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 173 nu = 0.008588 obj = -1.442637, rho = 0.554009 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 173 nu = 0.005970 obj = -1.442637, rho = 0.554009 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 173 nu = 0.004151 obj = -1.442637, rho = 0.554009 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 173 nu = 0.002885 obj = -1.442637, rho = 0.554009 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 50 nu = 0.640000 obj = -0.451697, rho = -0.146373 nSV = 65, nBSV = 62 Total nSV = 65 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 60 nu = 0.554801 obj = -0.554164, rho = -0.170066 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 43 nu = 0.465778 obj = -0.679296, rho = -0.185852 nSV = 51, nBSV = 43 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 46 nu = 0.400569 obj = -0.838778, rho = -0.137235 nSV = 43, nBSV = 39 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 78 nu = 0.349310 obj = -1.030916, rho = -0.119464 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 93 nu = 0.297445 obj = -1.254246, rho = -0.124432 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.249661 obj = -1.525268, rho = -0.055561 nSV = 30, nBSV = 21 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 87 nu = 0.214806 obj = -1.855467, rho = -0.101156 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 72 nu = 0.176632 obj = -2.255641, rho = -0.059871 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 78 nu = 0.151522 obj = -2.769397, rho = -0.111309 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..* optimization finished, #iter = 268 nu = 0.126724 obj = -3.403969, rho = -0.109062 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.108335 obj = -4.229664, rho = 0.019216 nSV = 18, nBSV = 7 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) ..*.* optimization finished, #iter = 398 nu = 0.091149 obj = -5.290166, rho = 0.051325 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 179 nu = 0.080172 obj = -6.757910, rho = 0.137453 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 136 nu = 0.076385 obj = -8.416262, rho = 0.397338 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.1% (961/1000) (classification) .*.* optimization finished, #iter = 272 nu = 0.066920 obj = -9.928755, rho = 0.532729 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 95.1% (951/1000) (classification) ........*........* optimization finished, #iter = 1670 nu = 0.052430 obj = -11.754918, rho = 0.487612 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 95.2% (952/1000) (classification) .* optimization finished, #iter = 174 nu = 0.043696 obj = -14.288479, rho = 0.631199 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 94.9% (949/1000) (classification) . WARNING: using -h 0 may be faster *.* optimization finished, #iter = 209 nu = 0.041253 obj = -16.591671, rho = 1.201108 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.1% (941/1000) (classification) ..*.* optimization finished, #iter = 384 nu = 0.034200 obj = -17.104087, rho = 1.580887 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.1% (941/1000) (classification) * optimization finished, #iter = 53 nu = 0.614650 obj = -0.419324, rho = -0.176891 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 89 nu = 0.523205 obj = -0.505094, rho = -0.134619 nSV = 57, nBSV = 49 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 88 nu = 0.438549 obj = -0.609287, rho = -0.082178 nSV = 48, nBSV = 40 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.365878 obj = -0.738332, rho = -0.072779 nSV = 41, nBSV = 33 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.306905 obj = -0.891787, rho = -0.062874 nSV = 36, nBSV = 27 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 51 nu = 0.261099 obj = -1.082546, rho = -0.007297 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 75 nu = 0.221630 obj = -1.299580, rho = -0.075618 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 99.3% (993/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.178115 obj = -1.563707, rho = -0.081920 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 66 nu = 0.149352 obj = -1.925325, rho = 0.073544 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.127451 obj = -2.375827, rho = 0.167538 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 67 nu = 0.109594 obj = -2.947830, rho = 0.167663 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 92 nu = 0.093817 obj = -3.669710, rho = 0.142853 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) ..* optimization finished, #iter = 272 nu = 0.081025 obj = -4.576595, rho = 0.056499 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*.* optimization finished, #iter = 306 nu = 0.067723 obj = -5.783285, rho = 0.060503 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..* optimization finished, #iter = 235 nu = 0.058494 obj = -7.477316, rho = 0.081172 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.052384 obj = -9.792521, rho = 0.151811 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.048217 obj = -12.855917, rho = 0.209792 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 239 nu = 0.046283 obj = -16.593892, rho = 0.081498 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) ..*.* optimization finished, #iter = 369 nu = 0.045329 obj = -20.373710, rho = -0.299811 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) ....*..* optimization finished, #iter = 603 nu = 0.037612 obj = -23.635348, rho = -0.574441 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 67 nu = 0.586334 obj = -0.391005, rho = -0.260339 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 68 nu = 0.486135 obj = -0.470056, rho = -0.198431 nSV = 53, nBSV = 45 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 48 nu = 0.409686 obj = -0.560825, rho = -0.167969 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 77 nu = 0.336206 obj = -0.670983, rho = -0.154899 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 37 nu = 0.291755 obj = -0.796166, rho = -0.039241 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.234947 obj = -0.929503, rho = 0.025219 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.190369 obj = -1.099562, rho = 0.053127 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *..* optimization finished, #iter = 214 nu = 0.151683 obj = -1.315991, rho = 0.088229 nSV = 22, nBSV = 11 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 86 nu = 0.129323 obj = -1.595350, rho = 0.192298 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 143 nu = 0.110717 obj = -1.904640, rho = 0.284374 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 184 nu = 0.089241 obj = -2.277811, rho = 0.257582 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.074364 obj = -2.734746, rho = 0.133152 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 153 nu = 0.061027 obj = -3.330002, rho = 0.047707 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 183 nu = 0.051148 obj = -4.123323, rho = 0.032208 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 152 nu = 0.045712 obj = -5.104887, rho = -0.174498 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*..* optimization finished, #iter = 300 nu = 0.038102 obj = -6.199914, rho = -0.240263 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 237 nu = 0.035439 obj = -7.427579, rho = -0.214559 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) .*.* optimization finished, #iter = 243 nu = 0.033444 obj = -8.083831, rho = -0.272281 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) .*.* optimization finished, #iter = 243 nu = 0.023250 obj = -8.083831, rho = -0.272281 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) .*.* optimization finished, #iter = 243 nu = 0.016163 obj = -8.083831, rho = -0.272281 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 34 nu = 0.574097 obj = -0.384960, rho = -0.068041 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 42 nu = 0.484172 obj = -0.462823, rho = -0.034170 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 35 nu = 0.412757 obj = -0.550637, rho = -0.011283 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 66 nu = 0.340170 obj = -0.644759, rho = -0.056938 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.278368 obj = -0.756184, rho = -0.130685 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 76 nu = 0.227864 obj = -0.886299, rho = -0.170354 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 67 nu = 0.180179 obj = -1.041596, rho = -0.140075 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.148161 obj = -1.227237, rho = -0.152249 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 68 nu = 0.122527 obj = -1.454755, rho = -0.175410 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 96 nu = 0.107710 obj = -1.682442, rho = 0.048066 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 154 nu = 0.081879 obj = -1.895140, rho = 0.065719 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 80 nu = 0.064137 obj = -2.169698, rho = 0.130408 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.051072 obj = -2.518153, rho = 0.273546 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.043065 obj = -2.869739, rho = 0.512707 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .* optimization finished, #iter = 169 nu = 0.037579 obj = -3.051108, rho = 0.855929 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) .* optimization finished, #iter = 169 nu = 0.026124 obj = -3.051108, rho = 0.855929 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) .* optimization finished, #iter = 169 nu = 0.018161 obj = -3.051108, rho = 0.855929 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) .* optimization finished, #iter = 169 nu = 0.012626 obj = -3.051108, rho = 0.855929 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) .* optimization finished, #iter = 169 nu = 0.008777 obj = -3.051108, rho = 0.855929 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) .* optimization finished, #iter = 169 nu = 0.006102 obj = -3.051108, rho = 0.855929 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 53 nu = 0.626751 obj = -0.407036, rho = 0.011936 nSV = 66, nBSV = 60 Total nSV = 66 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 48 nu = 0.506800 obj = -0.483551, rho = 0.018969 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 52 nu = 0.427336 obj = -0.576843, rho = -0.039225 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 99.4% (994/1000) (classification) * optimization finished, #iter = 61 nu = 0.356468 obj = -0.681282, rho = 0.023879 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 63 nu = 0.288770 obj = -0.801745, rho = 0.095994 nSV = 34, nBSV = 24 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 90 nu = 0.245815 obj = -0.930719, rho = 0.160510 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.196350 obj = -1.065957, rho = 0.207821 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 55 nu = 0.159940 obj = -1.200569, rho = 0.292410 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 52 nu = 0.123561 obj = -1.347635, rho = 0.335359 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 60 nu = 0.099113 obj = -1.486017, rho = 0.290400 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 89 nu = 0.075972 obj = -1.610632, rho = 0.227215 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 160 nu = 0.058946 obj = -1.697132, rho = 0.216146 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) ...*.* optimization finished, #iter = 416 nu = 0.044034 obj = -1.728045, rho = 0.192771 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) ...*.* optimization finished, #iter = 416 nu = 0.030612 obj = -1.728045, rho = 0.192771 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) ...*.* optimization finished, #iter = 416 nu = 0.021281 obj = -1.728045, rho = 0.192771 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) ...*.* optimization finished, #iter = 416 nu = 0.014795 obj = -1.728045, rho = 0.192771 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) ...*.* optimization finished, #iter = 416 nu = 0.010285 obj = -1.728045, rho = 0.192771 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) ...*.* optimization finished, #iter = 416 nu = 0.007150 obj = -1.728045, rho = 0.192771 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) ...*.* optimization finished, #iter = 416 nu = 0.004971 obj = -1.728045, rho = 0.192771 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) ...*.* optimization finished, #iter = 416 nu = 0.003456 obj = -1.728045, rho = 0.192771 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 34 nu = 0.640000 obj = -0.436577, rho = 0.059010 nSV = 65, nBSV = 63 Total nSV = 65 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 60 nu = 0.545143 obj = -0.527055, rho = 0.036236 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 54 nu = 0.454933 obj = -0.636201, rho = 0.017609 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 42 nu = 0.375036 obj = -0.772966, rho = 0.047787 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 46 nu = 0.322200 obj = -0.940804, rho = -0.065314 nSV = 35, nBSV = 30 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.270647 obj = -1.141788, rho = -0.002409 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 64 nu = 0.230949 obj = -1.380016, rho = -0.028955 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 49 nu = 0.190900 obj = -1.682498, rho = -0.002579 nSV = 21, nBSV = 17 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.162727 obj = -2.055528, rho = -0.110137 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.136507 obj = -2.511129, rho = 0.002211 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 73 nu = 0.114797 obj = -3.111324, rho = 0.020186 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.100248 obj = -3.863371, rho = 0.085802 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 165 nu = 0.085395 obj = -4.758860, rho = 0.150461 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 157 nu = 0.076480 obj = -5.862502, rho = 0.109518 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 138 nu = 0.068392 obj = -6.888480, rho = 0.046379 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .* optimization finished, #iter = 137 nu = 0.060156 obj = -7.607395, rho = 0.846590 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) ..*.* optimization finished, #iter = 320 nu = 0.046160 obj = -7.755996, rho = 1.146709 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) ..*.* optimization finished, #iter = 320 nu = 0.032090 obj = -7.755996, rho = 1.146709 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) ..*.* optimization finished, #iter = 320 nu = 0.022309 obj = -7.755996, rho = 1.146709 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) ..*.* optimization finished, #iter = 320 nu = 0.015509 obj = -7.755996, rho = 1.146709 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 29 nu = 0.560000 obj = -0.384069, rho = -0.349641 nSV = 56, nBSV = 56 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 33 nu = 0.480000 obj = -0.467309, rho = -0.361376 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 50 nu = 0.400000 obj = -0.564375, rho = -0.354095 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 78 nu = 0.347418 obj = -0.673780, rho = -0.382414 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 72 nu = 0.285290 obj = -0.795576, rho = -0.392020 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.234430 obj = -0.944757, rho = -0.386202 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 53 nu = 0.191946 obj = -1.124208, rho = -0.473260 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 58 nu = 0.155435 obj = -1.350040, rho = -0.484100 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 84 nu = 0.131866 obj = -1.638774, rho = -0.561572 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 57 nu = 0.110532 obj = -1.992225, rho = -0.754134 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 66 nu = 0.094686 obj = -2.393771, rho = -1.075451 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 86 nu = 0.081265 obj = -2.857701, rho = -1.073814 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.068847 obj = -3.315726, rho = -1.186913 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 95.8% (958/1000) (classification) .* optimization finished, #iter = 193 nu = 0.056705 obj = -3.736840, rho = -1.346537 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) .*.* optimization finished, #iter = 231 nu = 0.048866 obj = -3.968093, rho = -1.557918 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 93.4% (934/1000) (classification) .*.* optimization finished, #iter = 231 nu = 0.033971 obj = -3.968093, rho = -1.557918 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 93.4% (934/1000) (classification) .*.* optimization finished, #iter = 231 nu = 0.023617 obj = -3.968093, rho = -1.557918 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 93.4% (934/1000) (classification) .*.* optimization finished, #iter = 231 nu = 0.016418 obj = -3.968093, rho = -1.557918 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 93.4% (934/1000) (classification) .*.* optimization finished, #iter = 231 nu = 0.011414 obj = -3.968093, rho = -1.557918 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 93.4% (934/1000) (classification) .*.* optimization finished, #iter = 231 nu = 0.007935 obj = -3.968093, rho = -1.557918 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 93.4% (934/1000) (classification) * optimization finished, #iter = 53 nu = 0.610011 obj = -0.434600, rho = -0.110974 nSV = 65, nBSV = 59 Total nSV = 65 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 34 nu = 0.532990 obj = -0.536023, rho = -0.126137 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 48 nu = 0.454400 obj = -0.660840, rho = -0.053532 nSV = 49, nBSV = 42 Total nSV = 49 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 47 nu = 0.389559 obj = -0.817207, rho = -0.091479 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 46 nu = 0.332395 obj = -1.012652, rho = -0.102863 nSV = 35, nBSV = 30 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 136 nu = 0.282989 obj = -1.257204, rho = -0.034358 nSV = 33, nBSV = 23 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 97 nu = 0.241905 obj = -1.576843, rho = 0.057461 nSV = 28, nBSV = 18 Total nSV = 28 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 165 nu = 0.207529 obj = -2.007993, rho = 0.051446 nSV = 28, nBSV = 17 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 73 nu = 0.182181 obj = -2.584077, rho = -0.016412 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 30 nu = 0.170987 obj = -3.327000, rho = -0.239397 nSV = 18, nBSV = 14 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 77 nu = 0.154163 obj = -4.144927, rho = -0.398409 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) .**.* optimization finished, #iter = 185 nu = 0.136890 obj = -5.094112, rho = -0.581461 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 171 nu = 0.119034 obj = -6.173084, rho = -0.753723 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) ...*.* optimization finished, #iter = 485 nu = 0.096177 obj = -7.387434, rho = -0.639826 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) .....*..........*...* optimization finished, #iter = 1850 nu = 0.084808 obj = -8.841002, rho = -0.619842 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) .......*.* optimization finished, #iter = 881 nu = 0.067543 obj = -10.519162, rho = -0.639994 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*....* optimization finished, #iter = 536 nu = 0.057647 obj = -12.466636, rho = -0.593651 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .*.* optimization finished, #iter = 292 nu = 0.054028 obj = -13.997888, rho = -0.924089 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) .*.* optimization finished, #iter = 254 nu = 0.041749 obj = -14.515934, rho = -1.202462 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) .*.* optimization finished, #iter = 254 nu = 0.029024 obj = -14.515934, rho = -1.202462 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 59 nu = 0.625709 obj = -0.432880, rho = -0.181658 nSV = 66, nBSV = 61 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 32 nu = 0.540000 obj = -0.526767, rho = -0.198803 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 52 nu = 0.454978 obj = -0.638439, rho = -0.197084 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 50 nu = 0.382449 obj = -0.771233, rho = -0.243867 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 47 nu = 0.328474 obj = -0.923908, rho = -0.252344 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 80 nu = 0.266551 obj = -1.104268, rho = -0.204505 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 92 nu = 0.216664 obj = -1.344521, rho = -0.235036 nSV = 29, nBSV = 19 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 73 nu = 0.185504 obj = -1.653365, rho = -0.164293 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.156566 obj = -2.050329, rho = -0.153569 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 181 nu = 0.137357 obj = -2.552956, rho = -0.201175 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 96.4% (964/1000) (classification) ..*.* optimization finished, #iter = 357 nu = 0.121426 obj = -3.095803, rho = -0.277669 nSV = 18, nBSV = 7 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 96.4% (964/1000) (classification) ....*.* optimization finished, #iter = 577 nu = 0.097932 obj = -3.782274, rho = -0.263692 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) ..*.* optimization finished, #iter = 304 nu = 0.081675 obj = -4.744229, rho = -0.264629 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 96.1% (961/1000) (classification) ....*.* optimization finished, #iter = 507 nu = 0.068739 obj = -6.093860, rho = -0.264572 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 96.1% (961/1000) (classification) ....* optimization finished, #iter = 486 nu = 0.059744 obj = -8.035236, rho = -0.264532 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 96.1% (961/1000) (classification) .* optimization finished, #iter = 167 nu = 0.053912 obj = -10.822527, rho = -0.304759 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 98% (98/100) (classification) Accuracy = 96.2% (962/1000) (classification) .* optimization finished, #iter = 146 nu = 0.051194 obj = -14.651626, rho = -0.448832 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 98% (98/100) (classification) Accuracy = 96.4% (964/1000) (classification) .* optimization finished, #iter = 164 nu = 0.049305 obj = -19.668401, rho = -0.656498 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 95.9% (959/1000) (classification) .* optimization finished, #iter = 192 nu = 0.047991 obj = -25.868435, rho = -0.954839 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 95.4% (954/1000) (classification) .............* optimization finished, #iter = 1394 nu = 0.043498 obj = -33.434042, rho = -1.102516 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 63 nu = 0.605153 obj = -0.424700, rho = -0.227395 nSV = 64, nBSV = 57 Total nSV = 64 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 49 nu = 0.527884 obj = -0.522415, rho = -0.157823 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 51 nu = 0.448385 obj = -0.634630, rho = -0.256097 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 80 nu = 0.375704 obj = -0.774963, rho = -0.315969 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 38 nu = 0.317306 obj = -0.954864, rho = -0.362095 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 55 nu = 0.272279 obj = -1.171983, rho = -0.326872 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 88 nu = 0.230912 obj = -1.442890, rho = -0.343181 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 97% (97/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 90 nu = 0.197193 obj = -1.777653, rho = -0.331663 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.168922 obj = -2.206251, rho = -0.301600 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 146 nu = 0.145444 obj = -2.743456, rho = -0.309382 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) *..* optimization finished, #iter = 265 nu = 0.128968 obj = -3.373624, rho = -0.384031 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 166 nu = 0.106811 obj = -4.172238, rho = -0.456772 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) ..*......* optimization finished, #iter = 807 nu = 0.089259 obj = -5.240845, rho = -0.499539 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 99 nu = 0.078123 obj = -6.740975, rho = -0.402815 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.069253 obj = -8.627470, rho = -0.267534 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 146 nu = 0.060260 obj = -11.219603, rho = -0.388938 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..*.* optimization finished, #iter = 316 nu = 0.057137 obj = -14.581972, rho = -0.930492 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..*.* optimization finished, #iter = 329 nu = 0.050785 obj = -18.527171, rho = -1.229834 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..*.....................* optimization finished, #iter = 2383 nu = 0.043338 obj = -23.984064, rho = -1.188079 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) ...* optimization finished, #iter = 343 nu = 0.040610 obj = -31.388353, rho = -1.221074 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 62 nu = 0.590725 obj = -0.394948, rho = -0.206120 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 53 nu = 0.496724 obj = -0.476168, rho = -0.161741 nSV = 52, nBSV = 45 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 73 nu = 0.412198 obj = -0.571186, rho = -0.180088 nSV = 47, nBSV = 38 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 46 nu = 0.338277 obj = -0.691341, rho = -0.133352 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 50 nu = 0.284438 obj = -0.843169, rho = -0.181063 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 56 nu = 0.243847 obj = -1.028420, rho = -0.165691 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.208739 obj = -1.244600, rho = -0.105852 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 72 nu = 0.177399 obj = -1.501475, rho = -0.075790 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*..* optimization finished, #iter = 345 nu = 0.147093 obj = -1.791198, rho = -0.015484 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) ..* optimization finished, #iter = 259 nu = 0.119363 obj = -2.166118, rho = 0.053115 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 200 nu = 0.103253 obj = -2.629355, rho = 0.096665 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 97 nu = 0.083481 obj = -3.195364, rho = 0.083600 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 65 nu = 0.070182 obj = -3.984149, rho = 0.008077 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 71 nu = 0.062430 obj = -4.975065, rho = -0.262009 nSV = 10, nBSV = 4 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.059706 obj = -5.933410, rho = -0.520489 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.047722 obj = -6.676773, rho = -0.514136 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 201 nu = 0.036581 obj = -7.593961, rho = -0.553157 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 215 nu = 0.032132 obj = -8.432353, rho = -0.430065 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) ..*.* optimization finished, #iter = 331 nu = 0.024527 obj = -8.528216, rho = -0.372606 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.2% (952/1000) (classification) ..*.* optimization finished, #iter = 331 nu = 0.017051 obj = -8.528216, rho = -0.372606 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 43 nu = 0.605659 obj = -0.434447, rho = -0.218463 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 33 nu = 0.535136 obj = -0.539836, rho = -0.209920 nSV = 54, nBSV = 52 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 36 nu = 0.463830 obj = -0.662525, rho = -0.208064 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 29 nu = 0.399635 obj = -0.815260, rho = -0.271764 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.336508 obj = -0.989981, rho = -0.223359 nSV = 38, nBSV = 30 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 27 nu = 0.283900 obj = -1.212069, rho = -0.225779 nSV = 31, nBSV = 27 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 54 nu = 0.244274 obj = -1.466021, rho = -0.222873 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 52 nu = 0.205615 obj = -1.775296, rho = -0.371605 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 83 nu = 0.175245 obj = -2.132846, rho = -0.563127 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 90 nu = 0.152606 obj = -2.484961, rho = -0.633482 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 198 nu = 0.121566 obj = -2.855380, rho = -0.698511 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 195 nu = 0.095172 obj = -3.303238, rho = -0.719888 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 173 nu = 0.078353 obj = -3.838029, rho = -0.756455 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..* optimization finished, #iter = 233 nu = 0.063350 obj = -4.461569, rho = -0.716018 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*..* optimization finished, #iter = 437 nu = 0.050629 obj = -5.126844, rho = -0.778010 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ...*..* optimization finished, #iter = 516 nu = 0.040299 obj = -6.003296, rho = -0.826068 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*.* optimization finished, #iter = 338 nu = 0.032895 obj = -6.994696, rho = -0.795781 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.028982 obj = -8.011583, rho = -0.670567 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.023628 obj = -8.214003, rho = -0.527492 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.016426 obj = -8.214003, rho = -0.527492 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 49 nu = 0.555775 obj = -0.377960, rho = -0.018096 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 36 nu = 0.468777 obj = -0.460008, rho = 0.002024 nSV = 48, nBSV = 45 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 58 nu = 0.399681 obj = -0.556508, rho = -0.021220 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 84 nu = 0.332184 obj = -0.671052, rho = -0.048333 nSV = 37, nBSV = 28 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 76 nu = 0.274603 obj = -0.820644, rho = -0.093887 nSV = 32, nBSV = 23 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 39 nu = 0.236681 obj = -1.009553, rho = -0.132362 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.201724 obj = -1.238679, rho = -0.166484 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.169843 obj = -1.519650, rho = -0.181270 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.144610 obj = -1.880429, rho = -0.234403 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.124098 obj = -2.341247, rho = -0.250847 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.105241 obj = -2.934854, rho = -0.207380 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 186 nu = 0.093313 obj = -3.712163, rho = -0.075920 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 217 nu = 0.087129 obj = -4.574963, rho = 0.217795 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.078726 obj = -5.383778, rho = 0.380558 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 131 nu = 0.063747 obj = -6.069743, rho = 0.470899 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ....*...* optimization finished, #iter = 796 nu = 0.048722 obj = -6.759202, rho = 0.553364 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ...*...* optimization finished, #iter = 604 nu = 0.041776 obj = -7.320990, rho = 0.873892 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .*...* optimization finished, #iter = 455 nu = 0.030373 obj = -7.338778, rho = 0.871320 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .*...* optimization finished, #iter = 455 nu = 0.021115 obj = -7.338778, rho = 0.871320 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .*...* optimization finished, #iter = 455 nu = 0.014679 obj = -7.338778, rho = 0.871320 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 43 nu = 0.597133 obj = -0.406575, rho = -0.187659 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 40 nu = 0.496057 obj = -0.495073, rho = -0.242178 nSV = 54, nBSV = 47 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 156 nu = 0.430303 obj = -0.600494, rho = -0.219770 nSV = 47, nBSV = 39 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 46 nu = 0.359424 obj = -0.724664, rho = -0.179060 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.310627 obj = -0.865778, rho = -0.125674 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 43 nu = 0.253589 obj = -1.028586, rho = -0.225553 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 71 nu = 0.210006 obj = -1.222651, rho = -0.230097 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.173258 obj = -1.463349, rho = -0.222539 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 77 nu = 0.146811 obj = -1.745511, rho = -0.280104 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 77 nu = 0.129294 obj = -2.026196, rho = -0.516560 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ...*.* optimization finished, #iter = 408 nu = 0.108768 obj = -2.155594, rho = -0.836411 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .......*.* optimization finished, #iter = 863 nu = 0.078381 obj = -2.212331, rho = -0.857306 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ....*....* optimization finished, #iter = 801 nu = 0.055404 obj = -2.274497, rho = -0.849860 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 272 nu = 0.041070 obj = -2.349304, rho = -0.817699 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..*.........* optimization finished, #iter = 1174 nu = 0.028954 obj = -2.350535, rho = -0.809578 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..*.........* optimization finished, #iter = 1174 nu = 0.020128 obj = -2.350535, rho = -0.809578 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..*.........* optimization finished, #iter = 1174 nu = 0.013993 obj = -2.350535, rho = -0.809578 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..*.........* optimization finished, #iter = 1174 nu = 0.009728 obj = -2.350535, rho = -0.809578 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..*.........* optimization finished, #iter = 1174 nu = 0.006763 obj = -2.350535, rho = -0.809578 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..*.........* optimization finished, #iter = 1174 nu = 0.004701 obj = -2.350535, rho = -0.809578 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 35 nu = 0.580000 obj = -0.371744, rho = -0.111894 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 41 nu = 0.473806 obj = -0.435821, rho = -0.117133 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 65 nu = 0.388508 obj = -0.510123, rho = -0.098074 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 76 nu = 0.317772 obj = -0.597623, rho = -0.111695 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 29 nu = 0.261296 obj = -0.697793, rho = -0.143071 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.209484 obj = -0.809033, rho = -0.136975 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 33 nu = 0.168919 obj = -0.942196, rho = -0.147431 nSV = 20, nBSV = 16 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 81 nu = 0.143557 obj = -1.076973, rho = -0.189429 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 65 nu = 0.111352 obj = -1.205012, rho = -0.233194 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 181 nu = 0.088363 obj = -1.332590, rho = -0.210799 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 78 nu = 0.070172 obj = -1.439075, rho = -0.313199 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.052467 obj = -1.510229, rho = -0.444375 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) *.* optimization finished, #iter = 175 nu = 0.038142 obj = -1.568189, rho = -0.509832 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*............* optimization finished, #iter = 1320 nu = 0.028126 obj = -1.587923, rho = -0.645610 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .*............* optimization finished, #iter = 1320 nu = 0.019553 obj = -1.587923, rho = -0.645610 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .*............* optimization finished, #iter = 1320 nu = 0.013593 obj = -1.587923, rho = -0.645610 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .*............* optimization finished, #iter = 1320 nu = 0.009450 obj = -1.587923, rho = -0.645610 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .*............* optimization finished, #iter = 1320 nu = 0.006569 obj = -1.587923, rho = -0.645610 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .*............* optimization finished, #iter = 1320 nu = 0.004567 obj = -1.587923, rho = -0.645610 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .*............* optimization finished, #iter = 1320 nu = 0.003175 obj = -1.587923, rho = -0.645610 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 39 nu = 0.586582 obj = -0.405020, rho = -0.047303 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 57 nu = 0.511089 obj = -0.493411, rho = -0.036537 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 38 nu = 0.429229 obj = -0.595697, rho = -0.119143 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 50 nu = 0.371191 obj = -0.707014, rho = -0.114251 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 57 nu = 0.308374 obj = -0.834335, rho = -0.079083 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 81 nu = 0.252269 obj = -0.970419, rho = -0.033107 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.205946 obj = -1.117637, rho = 0.064150 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.164043 obj = -1.279082, rho = 0.052424 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 159 nu = 0.126703 obj = -1.473775, rho = 0.033775 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 95 nu = 0.106328 obj = -1.710571, rho = 0.063353 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 76 nu = 0.086893 obj = -1.914061, rho = 0.005258 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) .*.* optimization finished, #iter = 211 nu = 0.069199 obj = -2.065957, rho = 0.114834 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 180 nu = 0.051642 obj = -2.190184, rho = 0.013027 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 241 nu = 0.039833 obj = -2.248497, rho = -0.182723 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 241 nu = 0.027692 obj = -2.248497, rho = -0.182723 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 241 nu = 0.019251 obj = -2.248497, rho = -0.182723 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 241 nu = 0.013383 obj = -2.248497, rho = -0.182723 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 241 nu = 0.009304 obj = -2.248497, rho = -0.182723 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 241 nu = 0.006468 obj = -2.248497, rho = -0.182723 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 241 nu = 0.004497 obj = -2.248497, rho = -0.182723 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 44 nu = 0.603512 obj = -0.414942, rho = -0.274019 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 33 nu = 0.512031 obj = -0.504464, rho = -0.242865 nSV = 53, nBSV = 50 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 59 nu = 0.439190 obj = -0.609074, rho = -0.208144 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 46 nu = 0.361445 obj = -0.734318, rho = -0.215705 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 58 nu = 0.306274 obj = -0.890939, rho = -0.281412 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 52 nu = 0.259771 obj = -1.077060, rho = -0.297606 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 148 nu = 0.214406 obj = -1.301645, rho = -0.291912 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 38 nu = 0.180690 obj = -1.593627, rho = -0.255577 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 97 nu = 0.152638 obj = -1.948099, rho = -0.238845 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 134 nu = 0.129981 obj = -2.389091, rho = -0.289100 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 286 nu = 0.110365 obj = -2.945518, rho = -0.340648 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 189 nu = 0.097211 obj = -3.581927, rho = -0.280868 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 203 nu = 0.083069 obj = -4.313170, rho = -0.453454 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..* optimization finished, #iter = 248 nu = 0.070179 obj = -5.168045, rho = -0.433179 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) ...*.........* optimization finished, #iter = 1262 nu = 0.060964 obj = -5.943361, rho = -0.555446 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*....* optimization finished, #iter = 571 nu = 0.048649 obj = -6.684501, rho = -0.683693 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ...*..* optimization finished, #iter = 523 nu = 0.041449 obj = -7.190169, rho = -0.894935 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ..*.* optimization finished, #iter = 316 nu = 0.029972 obj = -7.242387, rho = -0.913300 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*.* optimization finished, #iter = 316 nu = 0.020836 obj = -7.242387, rho = -0.913300 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*.* optimization finished, #iter = 316 nu = 0.014485 obj = -7.242387, rho = -0.913300 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 36 nu = 0.597505 obj = -0.400306, rho = -0.025617 nSV = 60, nBSV = 57 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 42 nu = 0.500000 obj = -0.483207, rho = 0.013888 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 51 nu = 0.424782 obj = -0.576517, rho = -0.009675 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 51 nu = 0.351358 obj = -0.684666, rho = -0.058402 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 66 nu = 0.288642 obj = -0.817284, rho = -0.038836 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.244982 obj = -0.968964, rho = -0.040093 nSV = 28, nBSV = 18 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 89 nu = 0.194597 obj = -1.151149, rho = -0.074399 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 84 nu = 0.167263 obj = -1.375952, rho = -0.277260 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*..* optimization finished, #iter = 303 nu = 0.139891 obj = -1.599151, rho = -0.404085 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 198 nu = 0.110410 obj = -1.867000, rho = -0.308831 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 181 nu = 0.087311 obj = -2.211251, rho = -0.276951 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) ..*...* optimization finished, #iter = 503 nu = 0.072588 obj = -2.644440, rho = -0.247966 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) ...*......* optimization finished, #iter = 985 nu = 0.062593 obj = -3.145249, rho = -0.019477 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 95.6% (956/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.049550 obj = -3.706218, rho = 0.027220 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 95.4% (954/1000) (classification) .*.* optimization finished, #iter = 216 nu = 0.041700 obj = -4.408740, rho = -0.034398 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 60 nu = 0.036070 obj = -5.197151, rho = -0.042213 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.3% (943/1000) (classification) .*....* optimization finished, #iter = 564 nu = 0.033015 obj = -5.616360, rho = -0.040481 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 93% (930/1000) (classification) ...*......* optimization finished, #iter = 935 nu = 0.023255 obj = -5.619721, rho = -0.075753 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 93.1% (931/1000) (classification) ...*......* optimization finished, #iter = 935 nu = 0.016166 obj = -5.619721, rho = -0.075753 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 93.1% (931/1000) (classification) ...*......* optimization finished, #iter = 935 nu = 0.011239 obj = -5.619721, rho = -0.075753 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 93.1% (931/1000) (classification) * optimization finished, #iter = 47 nu = 0.598813 obj = -0.403024, rho = -0.014046 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 36 nu = 0.505643 obj = -0.487231, rho = -0.048101 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.425576 obj = -0.583218, rho = 0.041524 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 57 nu = 0.357067 obj = -0.695394, rho = 0.013393 nSV = 39, nBSV = 30 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 146 nu = 0.287825 obj = -0.832887, rho = 0.030281 nSV = 34, nBSV = 22 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 79 nu = 0.239229 obj = -1.013964, rho = 0.060349 nSV = 30, nBSV = 21 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 89 nu = 0.207057 obj = -1.233963, rho = 0.034543 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 69 nu = 0.175045 obj = -1.489500, rho = 0.123338 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.149792 obj = -1.778297, rho = -0.007334 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.122627 obj = -2.080867, rho = -0.111136 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 229 nu = 0.100205 obj = -2.440180, rho = -0.000379 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 89 nu = 0.084534 obj = -2.856398, rho = 0.405319 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 250 nu = 0.065965 obj = -3.306624, rho = 0.365225 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .*..* optimization finished, #iter = 348 nu = 0.055117 obj = -3.871220, rho = 0.346039 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 148 nu = 0.045051 obj = -4.366725, rho = 0.331902 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 160 nu = 0.034787 obj = -4.957232, rho = 0.234487 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 130 nu = 0.030186 obj = -5.545104, rho = 0.103722 nSV = 7, nBSV = 1 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.023189 obj = -5.605275, rho = 0.055907 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.016121 obj = -5.605275, rho = 0.055907 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.011207 obj = -5.605275, rho = 0.055907 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 44 nu = 0.601090 obj = -0.401400, rho = -0.119778 nSV = 63, nBSV = 57 Total nSV = 63 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 45 nu = 0.510887 obj = -0.480394, rho = -0.135805 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 49 nu = 0.430220 obj = -0.564559, rho = -0.153970 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 60 nu = 0.348761 obj = -0.657098, rho = -0.123605 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 63 nu = 0.293411 obj = -0.759371, rho = -0.067656 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 171 nu = 0.231480 obj = -0.860610, rho = -0.051890 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 56 nu = 0.184630 obj = -0.967455, rho = -0.076714 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 93 nu = 0.148320 obj = -1.077164, rho = -0.138695 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 150 nu = 0.112061 obj = -1.181465, rho = -0.151881 nSV = 18, nBSV = 7 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 275 nu = 0.083614 obj = -1.311069, rho = -0.151948 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *..* optimization finished, #iter = 240 nu = 0.067130 obj = -1.466895, rho = -0.148188 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 200 nu = 0.053807 obj = -1.590046, rho = -0.006097 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.041063 obj = -1.641792, rho = 0.058306 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 248 nu = 0.029126 obj = -1.643928, rho = 0.062803 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 248 nu = 0.020248 obj = -1.643928, rho = 0.062803 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 248 nu = 0.014076 obj = -1.643928, rho = 0.062803 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 248 nu = 0.009786 obj = -1.643928, rho = 0.062803 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 248 nu = 0.006803 obj = -1.643928, rho = 0.062803 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 248 nu = 0.004729 obj = -1.643928, rho = 0.062803 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 248 nu = 0.003288 obj = -1.643928, rho = 0.062803 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 38 nu = 0.598423 obj = -0.405434, rho = -0.121664 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.497582 obj = -0.492328, rho = -0.167323 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 36 nu = 0.420000 obj = -0.600202, rho = -0.204068 nSV = 43, nBSV = 40 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 39 nu = 0.360805 obj = -0.723974, rho = -0.175713 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.302248 obj = -0.874218, rho = -0.270260 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 74 nu = 0.252454 obj = -1.055868, rho = -0.388911 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.209708 obj = -1.285962, rho = -0.405945 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 87 nu = 0.177128 obj = -1.570715, rho = -0.439694 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.153512 obj = -1.925947, rho = -0.544914 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 75 nu = 0.125855 obj = -2.358622, rho = -0.584993 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 187 nu = 0.107197 obj = -2.932428, rho = -0.565949 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 73 nu = 0.092590 obj = -3.666861, rho = -0.552137 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 128 nu = 0.081829 obj = -4.580076, rho = -0.619411 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 80 nu = 0.069638 obj = -5.732157, rho = -0.817160 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 177 nu = 0.060167 obj = -7.224242, rho = -0.994082 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 170 nu = 0.051553 obj = -9.226911, rho = -1.065943 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 119 nu = 0.047988 obj = -11.822064, rho = -1.253589 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) ..*.* optimization finished, #iter = 390 nu = 0.045770 obj = -14.464969, rho = -1.591475 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 95.3% (953/1000) (classification) ...*.* optimization finished, #iter = 480 nu = 0.036527 obj = -17.298449, rho = -1.644260 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 94.9% (949/1000) (classification) .......* optimization finished, #iter = 756 nu = 0.031962 obj = -20.998152, rho = -1.705650 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 94% (940/1000) (classification) * optimization finished, #iter = 36 nu = 0.572039 obj = -0.385000, rho = -0.316421 nSV = 59, nBSV = 56 Total nSV = 59 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 59 nu = 0.486420 obj = -0.462249, rho = -0.320427 nSV = 51, nBSV = 44 Total nSV = 51 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 43 nu = 0.401208 obj = -0.551917, rho = -0.292917 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 51 nu = 0.334131 obj = -0.658098, rho = -0.278792 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 68 nu = 0.278568 obj = -0.785081, rho = -0.285554 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.225574 obj = -0.943235, rho = -0.298296 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 98 nu = 0.190792 obj = -1.132357, rho = -0.210723 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *..* optimization finished, #iter = 279 nu = 0.155307 obj = -1.373375, rho = -0.172652 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 95 nu = 0.129985 obj = -1.692577, rho = -0.247542 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 64 nu = 0.111442 obj = -2.103813, rho = -0.255652 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 230 nu = 0.095721 obj = -2.613607, rho = -0.311534 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 96.2% (962/1000) (classification) .* optimization finished, #iter = 185 nu = 0.082442 obj = -3.268440, rho = -0.337436 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 96.2% (962/1000) (classification) .* optimization finished, #iter = 197 nu = 0.071037 obj = -4.133675, rho = -0.339112 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 95.8% (958/1000) (classification) .* optimization finished, #iter = 144 nu = 0.062355 obj = -5.221562, rho = -0.267817 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 77 nu = 0.056768 obj = -6.608830, rho = -0.190803 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 87 nu = 0.047881 obj = -8.296097, rho = -0.323157 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 94.6% (946/1000) (classification) .* optimization finished, #iter = 183 nu = 0.041643 obj = -10.564568, rho = -0.381431 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 93.1% (931/1000) (classification) .* optimization finished, #iter = 181 nu = 0.035844 obj = -13.649137, rho = -0.354467 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 92.6% (926/1000) (classification) .* optimization finished, #iter = 145 nu = 0.032882 obj = -17.856546, rho = -0.399518 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 91.9% (919/1000) (classification) .*..* optimization finished, #iter = 321 nu = 0.030784 obj = -22.818782, rho = -0.501478 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 89.4% (894/1000) (classification) * optimization finished, #iter = 43 nu = 0.606863 obj = -0.422417, rho = -0.165292 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 62 nu = 0.535240 obj = -0.514328, rho = -0.185620 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 89 nu = 0.440103 obj = -0.618694, rho = -0.209030 nSV = 50, nBSV = 41 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 56 nu = 0.367594 obj = -0.749539, rho = -0.304198 nSV = 41, nBSV = 33 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 31 nu = 0.310620 obj = -0.914738, rho = -0.280979 nSV = 35, nBSV = 30 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 37 nu = 0.263354 obj = -1.113691, rho = -0.313619 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.221597 obj = -1.357304, rho = -0.303104 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 196 nu = 0.193052 obj = -1.646689, rho = -0.259513 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*...* optimization finished, #iter = 492 nu = 0.157002 obj = -1.997936, rho = -0.257625 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 131 nu = 0.133697 obj = -2.470250, rho = -0.308353 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 85 nu = 0.117327 obj = -3.008547, rho = -0.261700 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 89 nu = 0.101346 obj = -3.608198, rho = -0.055215 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 135 nu = 0.088816 obj = -4.165709, rho = -0.261795 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 281 nu = 0.067994 obj = -4.718727, rho = -0.329848 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*..* optimization finished, #iter = 307 nu = 0.052718 obj = -5.466598, rho = -0.356191 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 197 nu = 0.045485 obj = -6.288339, rho = -0.212304 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.036988 obj = -7.025369, rho = -0.320776 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.029697 obj = -7.177010, rho = -0.323602 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.020645 obj = -7.177010, rho = -0.323602 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.014352 obj = -7.177010, rho = -0.323602 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 84 nu = 0.618970 obj = -0.421633, rho = -0.072406 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 38 nu = 0.524480 obj = -0.511719, rho = -0.073417 nSV = 54, nBSV = 51 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 40 nu = 0.446042 obj = -0.618980, rho = -0.081736 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 46 nu = 0.370725 obj = -0.745575, rho = -0.063295 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 49 nu = 0.317338 obj = -0.896429, rho = -0.028745 nSV = 33, nBSV = 29 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 129 nu = 0.258060 obj = -1.068040, rho = -0.023427 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 69 nu = 0.220000 obj = -1.288374, rho = 0.108252 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 56 nu = 0.183861 obj = -1.520876, rho = 0.077104 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 134 nu = 0.154778 obj = -1.778502, rho = 0.075379 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.123537 obj = -2.070669, rho = 0.065086 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..* optimization finished, #iter = 235 nu = 0.101741 obj = -2.389373, rho = 0.015896 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..* optimization finished, #iter = 257 nu = 0.078369 obj = -2.785506, rho = 0.041486 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 132 nu = 0.065098 obj = -3.304533, rho = 0.027339 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 165 nu = 0.057396 obj = -3.779413, rho = -0.054651 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 219 nu = 0.047378 obj = -4.023228, rho = -0.046819 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 231 nu = 0.034618 obj = -4.043151, rho = -0.094741 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 231 nu = 0.024066 obj = -4.043151, rho = -0.094741 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 231 nu = 0.016730 obj = -4.043151, rho = -0.094741 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 231 nu = 0.011631 obj = -4.043151, rho = -0.094741 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 231 nu = 0.008086 obj = -4.043151, rho = -0.094741 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 39 nu = 0.564029 obj = -0.380180, rho = 0.017910 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 50 nu = 0.474396 obj = -0.458565, rho = 0.026569 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 50 nu = 0.398108 obj = -0.550408, rho = 0.109045 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.336750 obj = -0.658832, rho = 0.202292 nSV = 38, nBSV = 29 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 90 nu = 0.276698 obj = -0.784700, rho = 0.213720 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 47 nu = 0.229731 obj = -0.942189, rho = 0.293880 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.191473 obj = -1.131074, rho = 0.361527 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.158227 obj = -1.357080, rho = 0.403839 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 119 nu = 0.133727 obj = -1.632596, rho = 0.424077 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 198 nu = 0.109643 obj = -1.963527, rho = 0.409161 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 96.4% (964/1000) (classification) ....*..* optimization finished, #iter = 644 nu = 0.090334 obj = -2.401794, rho = 0.360157 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) ..*.* optimization finished, #iter = 381 nu = 0.074038 obj = -3.004485, rho = 0.358958 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*...* optimization finished, #iter = 432 nu = 0.063217 obj = -3.854360, rho = 0.355314 nSV = 16, nBSV = 4 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 161 nu = 0.057236 obj = -4.993788, rho = 0.317637 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96% (960/1000) (classification) ..*.* optimization finished, #iter = 389 nu = 0.052821 obj = -6.392015, rho = 0.267827 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 95.8% (958/1000) (classification) .* optimization finished, #iter = 165 nu = 0.046927 obj = -8.147986, rho = 0.155997 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 95.6% (956/1000) (classification) .* optimization finished, #iter = 164 nu = 0.042122 obj = -10.204885, rho = 0.159218 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 95.3% (953/1000) (classification) .*.* optimization finished, #iter = 241 nu = 0.036879 obj = -12.709857, rho = 0.410210 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 95.2% (952/1000) (classification) ..*.* optimization finished, #iter = 301 nu = 0.032933 obj = -15.657120, rho = 0.723556 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 94.7% (947/1000) (classification) ...*..*.* optimization finished, #iter = 648 nu = 0.030888 obj = -18.398846, rho = 1.551969 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 91.8% (918/1000) (classification) * optimization finished, #iter = 49 nu = 0.626751 obj = -0.435544, rho = -0.028445 nSV = 66, nBSV = 60 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 35 nu = 0.540000 obj = -0.535140, rho = -0.045502 nSV = 54, nBSV = 54 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 30 nu = 0.461593 obj = -0.650320, rho = -0.088082 nSV = 48, nBSV = 44 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 96 nu = 0.389492 obj = -0.787470, rho = -0.127352 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.330694 obj = -0.958278, rho = -0.176758 nSV = 35, nBSV = 31 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.282918 obj = -1.150083, rho = -0.176702 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 80 nu = 0.231834 obj = -1.379081, rho = -0.238581 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 35 nu = 0.195300 obj = -1.670533, rho = -0.058240 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 90 nu = 0.171239 obj = -1.972729, rho = -0.340112 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 74 nu = 0.137716 obj = -2.290766, rho = -0.352870 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 63 nu = 0.115745 obj = -2.618509, rho = -0.232686 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*.* optimization finished, #iter = 212 nu = 0.097034 obj = -2.876132, rho = -0.100849 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..* optimization finished, #iter = 283 nu = 0.070871 obj = -3.047859, rho = -0.098842 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*.* optimization finished, #iter = 337 nu = 0.053531 obj = -3.193558, rho = -0.147030 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) .*.* optimization finished, #iter = 210 nu = 0.040222 obj = -3.265946, rho = -0.140950 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .*.* optimization finished, #iter = 210 nu = 0.027962 obj = -3.265946, rho = -0.140950 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .*.* optimization finished, #iter = 210 nu = 0.019439 obj = -3.265946, rho = -0.140950 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .*.* optimization finished, #iter = 210 nu = 0.013514 obj = -3.265946, rho = -0.140950 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .*.* optimization finished, #iter = 210 nu = 0.009395 obj = -3.265946, rho = -0.140950 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .*.* optimization finished, #iter = 210 nu = 0.006531 obj = -3.265946, rho = -0.140950 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 38 nu = 0.593745 obj = -0.396501, rho = -0.318534 nSV = 61, nBSV = 58 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 37 nu = 0.504563 obj = -0.472418, rho = -0.299543 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 56 nu = 0.409145 obj = -0.562064, rho = -0.323937 nSV = 46, nBSV = 38 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 52 nu = 0.345874 obj = -0.668218, rho = -0.240830 nSV = 38, nBSV = 33 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.280264 obj = -0.792562, rho = -0.278497 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.229012 obj = -0.944346, rho = -0.253898 nSV = 30, nBSV = 19 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 39 nu = 0.193338 obj = -1.138180, rho = -0.297028 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 63 nu = 0.164763 obj = -1.347500, rho = -0.217441 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 58 nu = 0.134196 obj = -1.585842, rho = -0.230922 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 54 nu = 0.112483 obj = -1.825961, rho = -0.139034 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 96 nu = 0.094424 obj = -2.060698, rho = -0.290103 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.075448 obj = -2.231119, rho = -0.589769 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*..* optimization finished, #iter = 328 nu = 0.057785 obj = -2.298495, rho = -0.680194 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..* optimization finished, #iter = 243 nu = 0.040921 obj = -2.309980, rho = -0.658145 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..* optimization finished, #iter = 243 nu = 0.028448 obj = -2.309980, rho = -0.658145 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..* optimization finished, #iter = 243 nu = 0.019777 obj = -2.309980, rho = -0.658145 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..* optimization finished, #iter = 243 nu = 0.013749 obj = -2.309980, rho = -0.658145 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..* optimization finished, #iter = 243 nu = 0.009558 obj = -2.309980, rho = -0.658145 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..* optimization finished, #iter = 243 nu = 0.006645 obj = -2.309980, rho = -0.658145 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..* optimization finished, #iter = 243 nu = 0.004619 obj = -2.309980, rho = -0.658145 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 41 nu = 0.653627 obj = -0.448494, rho = -0.108502 nSV = 66, nBSV = 64 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 53 nu = 0.575179 obj = -0.537084, rho = -0.059527 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 45 nu = 0.480000 obj = -0.634191, rho = -0.088661 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 65 nu = 0.388061 obj = -0.741053, rho = -0.052992 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 67 nu = 0.320770 obj = -0.861766, rho = 0.044627 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.259361 obj = -0.995331, rho = 0.041682 nSV = 30, nBSV = 21 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.206517 obj = -1.151793, rho = 0.018779 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 195 nu = 0.169342 obj = -1.329434, rho = 0.061905 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 93 nu = 0.135381 obj = -1.536246, rho = -0.011065 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) ..*.* optimization finished, #iter = 399 nu = 0.107138 obj = -1.772645, rho = -0.015957 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) ..*....* optimization finished, #iter = 603 nu = 0.086382 obj = -2.049094, rho = -0.033347 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 287 nu = 0.072979 obj = -2.337328, rho = -0.129362 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 174 nu = 0.056089 obj = -2.575047, rho = -0.101629 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 224 nu = 0.043215 obj = -2.879036, rho = -0.105274 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 158 nu = 0.037866 obj = -3.079163, rho = -0.187541 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 170 nu = 0.026369 obj = -3.079171, rho = -0.187568 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 170 nu = 0.018331 obj = -3.079171, rho = -0.187568 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 170 nu = 0.012744 obj = -3.079171, rho = -0.187568 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 170 nu = 0.008859 obj = -3.079171, rho = -0.187568 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 170 nu = 0.006159 obj = -3.079171, rho = -0.187568 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 38 nu = 0.610273 obj = -0.417996, rho = -0.088147 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 37 nu = 0.526823 obj = -0.503969, rho = -0.037645 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 67 nu = 0.440698 obj = -0.602412, rho = 0.015571 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 81 nu = 0.360397 obj = -0.725018, rho = 0.014221 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 166 nu = 0.294757 obj = -0.883912, rho = 0.004508 nSV = 36, nBSV = 27 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 86 nu = 0.254485 obj = -1.080069, rho = 0.090263 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 73 nu = 0.219125 obj = -1.319385, rho = 0.183730 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.184121 obj = -1.590186, rho = 0.171682 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 183 nu = 0.151635 obj = -1.936752, rho = 0.141184 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.126825 obj = -2.394561, rho = 0.231002 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) *.* optimization finished, #iter = 160 nu = 0.112158 obj = -2.957022, rho = 0.292570 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 96.4% (964/1000) (classification) .*.* optimization finished, #iter = 278 nu = 0.095158 obj = -3.604883, rho = 0.202407 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 96.2% (962/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.080540 obj = -4.450045, rho = 0.226862 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) .* optimization finished, #iter = 143 nu = 0.072025 obj = -5.415677, rho = 0.257012 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) .*.....* optimization finished, #iter = 633 nu = 0.065655 obj = -6.343889, rho = 0.083892 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 95.9% (959/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.055817 obj = -6.897590, rho = -0.432865 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .*.* optimization finished, #iter = 294 nu = 0.041273 obj = -6.934099, rho = -0.626721 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .*.* optimization finished, #iter = 294 nu = 0.028693 obj = -6.934099, rho = -0.626721 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .*.* optimization finished, #iter = 294 nu = 0.019947 obj = -6.934099, rho = -0.626721 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .*.* optimization finished, #iter = 294 nu = 0.013867 obj = -6.934099, rho = -0.626721 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 52 nu = 0.598574 obj = -0.401162, rho = -0.177523 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 79 nu = 0.498697 obj = -0.482367, rho = -0.161656 nSV = 53, nBSV = 46 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 42 nu = 0.416380 obj = -0.580876, rho = -0.240834 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 34 nu = 0.347838 obj = -0.698413, rho = -0.149861 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 34 nu = 0.287395 obj = -0.849839, rho = -0.137084 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 36 nu = 0.249854 obj = -1.032798, rho = -0.241194 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 41 nu = 0.210468 obj = -1.231624, rho = -0.342044 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 59 nu = 0.177460 obj = -1.455616, rho = -0.414161 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 50 nu = 0.148457 obj = -1.705542, rho = -0.363671 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.122179 obj = -1.969574, rho = -0.406255 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 99 nu = 0.098297 obj = -2.239743, rho = -0.385371 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*..*.* optimization finished, #iter = 464 nu = 0.079073 obj = -2.457438, rho = -0.322655 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 79 nu = 0.058558 obj = -2.711383, rho = -0.365157 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 161 nu = 0.044938 obj = -3.057406, rho = -0.380138 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 168 nu = 0.037398 obj = -3.416431, rho = -0.392663 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 138 nu = 0.030435 obj = -3.614502, rho = -0.493028 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 180 nu = 0.021526 obj = -3.616512, rho = -0.509826 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 180 nu = 0.014965 obj = -3.616512, rho = -0.509826 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 180 nu = 0.010403 obj = -3.616512, rho = -0.509826 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 180 nu = 0.007232 obj = -3.616512, rho = -0.509826 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 69 nu = 0.605435 obj = -0.425928, rho = -0.054094 nSV = 66, nBSV = 57 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.525427 obj = -0.524052, rho = -0.012318 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 28 nu = 0.446230 obj = -0.642564, rho = 0.005438 nSV = 46, nBSV = 44 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 37 nu = 0.384986 obj = -0.783643, rho = 0.072303 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 46 nu = 0.330787 obj = -0.948902, rho = 0.071562 nSV = 34, nBSV = 30 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 54 nu = 0.276297 obj = -1.142533, rho = 0.141207 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 49 nu = 0.234172 obj = -1.373344, rho = 0.123016 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 65 nu = 0.201181 obj = -1.623136, rho = 0.102131 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 65 nu = 0.163946 obj = -1.882856, rho = 0.006926 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.130807 obj = -2.179463, rho = -0.012344 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 197 nu = 0.106381 obj = -2.531609, rho = -0.078378 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 178 nu = 0.086635 obj = -2.940025, rho = -0.083924 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 155 nu = 0.075024 obj = -3.305173, rho = -0.182946 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 234 nu = 0.056012 obj = -3.568361, rho = -0.211842 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 140 nu = 0.043515 obj = -3.760588, rho = -0.171940 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 164 nu = 0.032744 obj = -3.903174, rho = -0.270073 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 194 nu = 0.023260 obj = -3.908358, rho = -0.303249 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 194 nu = 0.016170 obj = -3.908358, rho = -0.303249 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 194 nu = 0.011241 obj = -3.908358, rho = -0.303249 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 194 nu = 0.007815 obj = -3.908358, rho = -0.303249 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 41 nu = 0.560000 obj = -0.384922, rho = -0.176701 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 40 nu = 0.481511 obj = -0.466856, rho = -0.104587 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 27 nu = 0.406579 obj = -0.564674, rho = -0.047338 nSV = 43, nBSV = 39 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 62 nu = 0.344008 obj = -0.674943, rho = 0.046812 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 45 nu = 0.285029 obj = -0.806693, rho = 0.015807 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 43 nu = 0.245297 obj = -0.957472, rho = 0.047110 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.199103 obj = -1.109964, rho = 0.012665 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 152 nu = 0.157615 obj = -1.294320, rho = 0.004898 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 50 nu = 0.126327 obj = -1.542268, rho = 0.034954 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 29 nu = 0.111766 obj = -1.816221, rho = 0.022495 nSV = 14, nBSV = 9 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 69 nu = 0.091289 obj = -2.035788, rho = 0.015601 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 80 nu = 0.073290 obj = -2.244269, rho = 0.073265 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 58 nu = 0.057258 obj = -2.421569, rho = 0.083075 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.044121 obj = -2.491812, rho = 0.125778 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 155 nu = 0.030694 obj = -2.491819, rho = 0.129418 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 155 nu = 0.021338 obj = -2.491819, rho = 0.129418 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 155 nu = 0.014834 obj = -2.491819, rho = 0.129418 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 155 nu = 0.010312 obj = -2.491819, rho = 0.129418 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 155 nu = 0.007169 obj = -2.491819, rho = 0.129418 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 155 nu = 0.004984 obj = -2.491819, rho = 0.129418 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.587562 obj = -0.407492, rho = -0.267540 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 37 nu = 0.511797 obj = -0.494531, rho = -0.267442 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 43 nu = 0.429209 obj = -0.597755, rho = -0.352108 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 76 nu = 0.366551 obj = -0.711353, rho = -0.476465 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 59 nu = 0.303316 obj = -0.843875, rho = -0.452247 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 69 nu = 0.248943 obj = -0.993431, rho = -0.501683 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.203044 obj = -1.181144, rho = -0.437577 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 91 nu = 0.171755 obj = -1.391680, rho = -0.412984 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) ..*.* optimization finished, #iter = 362 nu = 0.135737 obj = -1.631802, rho = -0.417082 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 124 nu = 0.108936 obj = -1.957138, rho = -0.390668 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 130 nu = 0.092487 obj = -2.387442, rho = -0.398431 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 185 nu = 0.077618 obj = -2.908155, rho = -0.472532 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.066991 obj = -3.514962, rho = -0.547840 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.055857 obj = -4.208667, rho = -0.745838 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.048065 obj = -4.978365, rho = -1.061421 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 91 nu = 0.039100 obj = -5.848980, rho = -1.295558 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 72 nu = 0.033478 obj = -6.766195, rho = -1.492989 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.029951 obj = -7.239726, rho = -1.914739 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.020821 obj = -7.239726, rho = -1.914739 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.014475 obj = -7.239726, rho = -1.914739 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 42 nu = 0.667257 obj = -0.459966, rho = -0.217117 nSV = 69, nBSV = 63 Total nSV = 69 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 45 nu = 0.569423 obj = -0.562377, rho = -0.226172 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 68 nu = 0.493598 obj = -0.681713, rho = -0.151358 nSV = 52, nBSV = 45 Total nSV = 52 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 51 nu = 0.408171 obj = -0.817827, rho = -0.151084 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 49 nu = 0.344373 obj = -0.987829, rho = -0.217818 nSV = 38, nBSV = 33 Total nSV = 38 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 41 nu = 0.286841 obj = -1.190332, rho = -0.215216 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 71 nu = 0.236079 obj = -1.443705, rho = -0.204167 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*..* optimization finished, #iter = 346 nu = 0.201051 obj = -1.758395, rho = -0.224685 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *..* optimization finished, #iter = 238 nu = 0.175548 obj = -2.127163, rho = -0.236901 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) ....* optimization finished, #iter = 456 nu = 0.143964 obj = -2.550439, rho = -0.267315 nSV = 20, nBSV = 9 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 246 nu = 0.117916 obj = -3.099775, rho = -0.227075 nSV = 19, nBSV = 8 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 165 nu = 0.102200 obj = -3.750577, rho = -0.137311 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 215 nu = 0.085963 obj = -4.504761, rho = -0.063801 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 180 nu = 0.073147 obj = -5.363262, rho = -0.073300 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 159 nu = 0.063561 obj = -6.226914, rho = -0.086144 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.050217 obj = -6.908265, rho = 0.005921 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) ...*.* optimization finished, #iter = 494 nu = 0.039467 obj = -7.710514, rho = 0.110897 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 186 nu = 0.033560 obj = -8.111486, rho = 0.204487 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 186 nu = 0.023330 obj = -8.111486, rho = 0.204487 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 186 nu = 0.016219 obj = -8.111486, rho = 0.204487 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 42 nu = 0.574761 obj = -0.385421, rho = -0.136809 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 42 nu = 0.486941 obj = -0.460799, rho = -0.076053 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 38 nu = 0.416677 obj = -0.542822, rho = -0.138000 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 51 nu = 0.332533 obj = -0.629723, rho = -0.135672 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 36 nu = 0.265454 obj = -0.741050, rho = -0.174207 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 39 nu = 0.219947 obj = -0.872913, rho = -0.312536 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 75 nu = 0.178075 obj = -1.023608, rho = -0.282550 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 59 nu = 0.149303 obj = -1.204684, rho = -0.185428 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 89 nu = 0.118293 obj = -1.411113, rho = -0.190182 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.095009 obj = -1.682924, rho = -0.209431 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 134 nu = 0.081124 obj = -2.025074, rho = -0.165955 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 173 nu = 0.070218 obj = -2.350356, rho = -0.054955 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 146 nu = 0.057626 obj = -2.659384, rho = 0.025170 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 83 nu = 0.046565 obj = -2.946209, rho = 0.126666 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 143 nu = 0.037335 obj = -3.032043, rho = 0.250589 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 143 nu = 0.025955 obj = -3.032043, rho = 0.250589 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 143 nu = 0.018044 obj = -3.032043, rho = 0.250589 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 143 nu = 0.012544 obj = -3.032043, rho = 0.250589 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 143 nu = 0.008720 obj = -3.032043, rho = 0.250589 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 143 nu = 0.006062 obj = -3.032043, rho = 0.250589 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 45 nu = 0.619465 obj = -0.422809, rho = -0.031671 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 41 nu = 0.518701 obj = -0.513892, rho = 0.003462 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 30 nu = 0.449431 obj = -0.626741, rho = 0.084025 nSV = 46, nBSV = 43 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 43 nu = 0.380575 obj = -0.750311, rho = 0.083086 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 47 nu = 0.317532 obj = -0.894050, rho = 0.056224 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.257924 obj = -1.068441, rho = 0.032261 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 60 nu = 0.219556 obj = -1.289988, rho = 0.026139 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 77 nu = 0.182173 obj = -1.541975, rho = -0.020807 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 58 nu = 0.149479 obj = -1.850013, rho = -0.058996 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 56 nu = 0.125426 obj = -2.254671, rho = -0.160706 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.111198 obj = -2.682596, rho = -0.257397 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 68 nu = 0.093181 obj = -3.109636, rho = -0.118847 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ...*..* optimization finished, #iter = 573 nu = 0.076669 obj = -3.478745, rho = -0.062885 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..*..* optimization finished, #iter = 403 nu = 0.060219 obj = -3.825402, rho = -0.016306 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..* optimization finished, #iter = 272 nu = 0.046813 obj = -4.080624, rho = 0.059146 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 249 nu = 0.035339 obj = -4.127458, rho = 0.112356 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*.* optimization finished, #iter = 249 nu = 0.024567 obj = -4.127458, rho = 0.112356 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*.* optimization finished, #iter = 249 nu = 0.017079 obj = -4.127458, rho = 0.112356 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*.* optimization finished, #iter = 249 nu = 0.011873 obj = -4.127458, rho = 0.112356 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*.* optimization finished, #iter = 249 nu = 0.008254 obj = -4.127458, rho = 0.112356 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 40 nu = 0.610761 obj = -0.404063, rho = -0.188549 nSV = 63, nBSV = 57 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 65 nu = 0.515157 obj = -0.478644, rho = -0.243119 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 31 nu = 0.427777 obj = -0.564615, rho = -0.207343 nSV = 44, nBSV = 41 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 64 nu = 0.347669 obj = -0.661355, rho = -0.216699 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.277182 obj = -0.779860, rho = -0.218263 nSV = 32, nBSV = 23 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 57 nu = 0.228226 obj = -0.931675, rho = -0.191313 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 60 nu = 0.193297 obj = -1.099304, rho = -0.105502 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 80 nu = 0.160265 obj = -1.275969, rho = -0.063747 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 60 nu = 0.128633 obj = -1.478389, rho = -0.238727 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 167 nu = 0.104245 obj = -1.709309, rho = -0.374206 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 87 nu = 0.083206 obj = -1.984042, rho = -0.406193 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.070951 obj = -2.252276, rho = -0.527882 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.058636 obj = -2.431643, rho = -0.690978 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.043727 obj = -2.474891, rho = -0.868076 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.030482 obj = -2.474969, rho = -0.867753 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.021191 obj = -2.474969, rho = -0.867753 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.014732 obj = -2.474969, rho = -0.867753 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.010242 obj = -2.474969, rho = -0.867753 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.007120 obj = -2.474969, rho = -0.867753 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.004950 obj = -2.474969, rho = -0.867753 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 76 nu = 0.605514 obj = -0.432148, rho = -0.126966 nSV = 66, nBSV = 57 Total nSV = 66 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 34 nu = 0.528815 obj = -0.538826, rho = -0.130712 nSV = 55, nBSV = 52 Total nSV = 55 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 38 nu = 0.462161 obj = -0.663577, rho = -0.177203 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 39 nu = 0.393308 obj = -0.808326, rho = -0.111869 nSV = 41, nBSV = 37 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 74 nu = 0.333413 obj = -0.991257, rho = -0.069462 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 75 nu = 0.281297 obj = -1.215685, rho = -0.034601 nSV = 34, nBSV = 24 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 71 nu = 0.239282 obj = -1.502783, rho = 0.013912 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.207634 obj = -1.865006, rho = -0.091136 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 56 nu = 0.176070 obj = -2.305361, rho = -0.049653 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *..* optimization finished, #iter = 297 nu = 0.153575 obj = -2.872006, rho = 0.067831 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..* optimization finished, #iter = 279 nu = 0.128267 obj = -3.595058, rho = 0.072889 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.111320 obj = -4.580829, rho = 0.133419 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 254 nu = 0.100324 obj = -5.834592, rho = 0.151627 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) ...*.* optimization finished, #iter = 410 nu = 0.085850 obj = -7.466022, rho = 0.134347 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..* optimization finished, #iter = 254 nu = 0.074402 obj = -9.745195, rho = 0.140346 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 174 nu = 0.071067 obj = -12.740126, rho = 0.162098 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) ...*.* optimization finished, #iter = 496 nu = 0.065406 obj = -16.234535, rho = 0.059566 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..*...* optimization finished, #iter = 520 nu = 0.057453 obj = -20.455954, rho = -0.274363 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .....*..* optimization finished, #iter = 701 nu = 0.049158 obj = -26.030065, rho = -0.312227 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .....*..* optimization finished, #iter = 767 nu = 0.044667 obj = -33.390676, rho = -0.314241 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 51 nu = 0.596205 obj = -0.409059, rho = -0.264170 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 66 nu = 0.498731 obj = -0.497417, rho = -0.235610 nSV = 53, nBSV = 46 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.423288 obj = -0.606900, rho = -0.150363 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 33 nu = 0.356565 obj = -0.748089, rho = -0.138770 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 53 nu = 0.307729 obj = -0.917150, rho = -0.250643 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 73 nu = 0.257193 obj = -1.132609, rho = -0.287542 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.223173 obj = -1.407834, rho = -0.325083 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 62 nu = 0.193439 obj = -1.743795, rho = -0.422605 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.166987 obj = -2.148568, rho = -0.511275 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 80 nu = 0.145668 obj = -2.632125, rho = -0.581770 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .*..* optimization finished, #iter = 356 nu = 0.126223 obj = -3.153841, rho = -0.476617 nSV = 19, nBSV = 8 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 176 nu = 0.101861 obj = -3.805517, rho = -0.556747 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 222 nu = 0.083524 obj = -4.662600, rho = -0.611078 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 198 nu = 0.071909 obj = -5.792223, rho = -0.556983 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 284 nu = 0.063700 obj = -7.180313, rho = -0.534570 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..*.* optimization finished, #iter = 320 nu = 0.058555 obj = -8.564873, rho = -0.638183 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*..* optimization finished, #iter = 385 nu = 0.053364 obj = -9.518641, rho = -0.906914 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ...*..* optimization finished, #iter = 541 nu = 0.039800 obj = -9.618771, rho = -1.093627 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) ...*..* optimization finished, #iter = 541 nu = 0.027669 obj = -9.618771, rho = -1.093627 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) ...*..* optimization finished, #iter = 541 nu = 0.019235 obj = -9.618771, rho = -1.093627 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 45 nu = 0.592501 obj = -0.409899, rho = -0.102225 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 96% (96/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 54 nu = 0.499145 obj = -0.502390, rho = -0.093527 nSV = 54, nBSV = 47 Total nSV = 54 Accuracy = 96% (96/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 52 nu = 0.425008 obj = -0.618308, rho = -0.081600 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.363255 obj = -0.763995, rho = -0.128045 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 52 nu = 0.317352 obj = -0.942333, rho = -0.212234 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.272050 obj = -1.149797, rho = -0.225220 nSV = 30, nBSV = 21 Total nSV = 30 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 90 nu = 0.225952 obj = -1.410878, rho = -0.189885 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 94 nu = 0.189258 obj = -1.749533, rho = -0.164964 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*..* optimization finished, #iter = 307 nu = 0.162493 obj = -2.203623, rho = -0.111878 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 260 nu = 0.141263 obj = -2.809807, rho = -0.133320 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 92 nu = 0.126515 obj = -3.591646, rho = -0.185355 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.114481 obj = -4.512933, rho = -0.256895 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 63 nu = 0.101849 obj = -5.623894, rho = -0.361953 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 63 nu = 0.094398 obj = -6.815088, rho = -0.677090 nSV = 12, nBSV = 7 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.077037 obj = -7.853956, rho = -0.500837 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 129 nu = 0.067395 obj = -8.860623, rho = -0.601195 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ......* optimization finished, #iter = 642 nu = 0.054280 obj = -9.117703, rho = -0.721574 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) ......* optimization finished, #iter = 642 nu = 0.037735 obj = -9.117703, rho = -0.721574 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) ......* optimization finished, #iter = 642 nu = 0.026233 obj = -9.117703, rho = -0.721574 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) ......* optimization finished, #iter = 642 nu = 0.018237 obj = -9.117703, rho = -0.721574 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 48 nu = 0.582366 obj = -0.380201, rho = -0.273815 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 53 nu = 0.488752 obj = -0.445513, rho = -0.296314 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 48 nu = 0.388880 obj = -0.523896, rho = -0.284912 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 71 nu = 0.326059 obj = -0.612803, rho = -0.247252 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 67 nu = 0.268294 obj = -0.706997, rho = -0.181179 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 70 nu = 0.216622 obj = -0.812984, rho = -0.261329 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 165 nu = 0.171543 obj = -0.927595, rho = -0.369961 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 162 nu = 0.131955 obj = -1.070220, rho = -0.369315 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 63 nu = 0.108375 obj = -1.245140, rho = -0.319451 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 80 nu = 0.090046 obj = -1.423944, rho = -0.282033 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.072829 obj = -1.599552, rho = -0.491200 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 54 nu = 0.056796 obj = -1.767782, rho = -0.618444 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 177 nu = 0.043406 obj = -1.912489, rho = -0.711500 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 136 nu = 0.033432 obj = -2.065190, rho = -0.747173 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.025917 obj = -2.104488, rho = -0.581837 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.018018 obj = -2.104488, rho = -0.581837 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.012526 obj = -2.104488, rho = -0.581837 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.008708 obj = -2.104488, rho = -0.581837 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.006054 obj = -2.104488, rho = -0.581837 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.004208 obj = -2.104488, rho = -0.581837 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 37 nu = 0.612442 obj = -0.418490, rho = -0.178975 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 52 nu = 0.520764 obj = -0.505578, rho = -0.144443 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 45 nu = 0.439208 obj = -0.609525, rho = -0.137617 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 54 nu = 0.367700 obj = -0.730856, rho = -0.137050 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 42 nu = 0.308598 obj = -0.874799, rho = -0.144917 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 89 nu = 0.253107 obj = -1.051251, rho = -0.199908 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 241 nu = 0.212883 obj = -1.259410, rho = -0.209732 nSV = 25, nBSV = 15 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 273 nu = 0.170504 obj = -1.535657, rho = -0.215149 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 74 nu = 0.146485 obj = -1.909541, rho = -0.278249 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 93 nu = 0.134523 obj = -2.319524, rho = -0.491845 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.111451 obj = -2.761002, rho = -0.560740 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.098503 obj = -3.185776, rho = -0.630636 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ....*........* optimization finished, #iter = 1216 nu = 0.078438 obj = -3.512894, rho = -0.623523 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 181 nu = 0.062167 obj = -3.782026, rho = -0.721519 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ...*..* optimization finished, #iter = 543 nu = 0.047375 obj = -3.848118, rho = -0.813363 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ...*...* optimization finished, #iter = 620 nu = 0.032948 obj = -3.848130, rho = -0.812799 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ...*...* optimization finished, #iter = 620 nu = 0.022905 obj = -3.848130, rho = -0.812799 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ...*...* optimization finished, #iter = 620 nu = 0.015923 obj = -3.848130, rho = -0.812799 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ...*...* optimization finished, #iter = 620 nu = 0.011070 obj = -3.848130, rho = -0.812799 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ...*...* optimization finished, #iter = 620 nu = 0.007696 obj = -3.848130, rho = -0.812799 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 37 nu = 0.609341 obj = -0.402631, rho = -0.138304 nSV = 62, nBSV = 59 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 41 nu = 0.503685 obj = -0.479170, rho = -0.124646 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.420532 obj = -0.568363, rho = -0.154211 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 70 nu = 0.338726 obj = -0.679818, rho = -0.172658 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 64 nu = 0.291730 obj = -0.817810, rho = -0.126615 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 89 nu = 0.238829 obj = -0.971931, rho = -0.137018 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 55 nu = 0.193663 obj = -1.172970, rho = -0.139884 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 66 nu = 0.165427 obj = -1.424714, rho = -0.114692 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 58 nu = 0.134185 obj = -1.740202, rho = -0.113423 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 75 nu = 0.120619 obj = -2.125520, rho = -0.012772 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 83 nu = 0.099493 obj = -2.550619, rho = -0.027237 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 74 nu = 0.083895 obj = -3.085963, rho = 0.003650 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.072853 obj = -3.673233, rho = -0.050404 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.059746 obj = -4.317529, rho = -0.064202 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 136 nu = 0.048713 obj = -5.027456, rho = -0.095906 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 143 nu = 0.039194 obj = -5.934693, rho = -0.086616 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 169 nu = 0.034438 obj = -6.855775, rho = 0.195165 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..*.* optimization finished, #iter = 371 nu = 0.030213 obj = -7.301073, rho = 0.642731 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..*.* optimization finished, #iter = 371 nu = 0.021004 obj = -7.301073, rho = 0.642731 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..*.* optimization finished, #iter = 371 nu = 0.014602 obj = -7.301073, rho = 0.642731 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 45 nu = 0.603391 obj = -0.416589, rho = -0.097926 nSV = 64, nBSV = 58 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 31 nu = 0.520000 obj = -0.509136, rho = -0.108880 nSV = 52, nBSV = 52 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 54 nu = 0.431782 obj = -0.617879, rho = -0.130882 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.362405 obj = -0.755792, rho = -0.130823 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 40 nu = 0.308099 obj = -0.930785, rho = -0.159745 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 63 nu = 0.264656 obj = -1.145300, rho = -0.126931 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 70 nu = 0.228642 obj = -1.404999, rho = -0.060503 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 50 nu = 0.198065 obj = -1.710893, rho = -0.119887 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 63 nu = 0.169616 obj = -2.068514, rho = -0.253339 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 98 nu = 0.141045 obj = -2.464453, rho = -0.420024 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 54 nu = 0.114499 obj = -2.955049, rho = -0.376434 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 95 nu = 0.096473 obj = -3.565733, rho = -0.297132 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 96 nu = 0.079682 obj = -4.362725, rho = -0.282883 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.073168 obj = -5.236348, rho = -0.641797 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) ...*.* optimization finished, #iter = 438 nu = 0.061431 obj = -5.918781, rho = -1.078732 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 238 nu = 0.047605 obj = -6.644139, rho = -1.144158 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 232 nu = 0.037203 obj = -7.514087, rho = -1.214284 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 241 nu = 0.030611 obj = -8.417137, rho = -1.297248 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*.* optimization finished, #iter = 349 nu = 0.025223 obj = -8.766386, rho = -1.384935 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..*.* optimization finished, #iter = 349 nu = 0.017535 obj = -8.766386, rho = -1.384935 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 67 nu = 0.569635 obj = -0.378632, rho = -0.124082 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 41 nu = 0.478023 obj = -0.449195, rho = -0.101918 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 39 nu = 0.399877 obj = -0.530830, rho = -0.110397 nSV = 41, nBSV = 37 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 46 nu = 0.328920 obj = -0.618840, rho = -0.095333 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 51 nu = 0.265199 obj = -0.716925, rho = -0.095782 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 51 nu = 0.214832 obj = -0.834141, rho = -0.082366 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 99 nu = 0.177967 obj = -0.958777, rho = -0.030159 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 40 nu = 0.142030 obj = -1.097092, rho = -0.064296 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 66 nu = 0.116845 obj = -1.222373, rho = -0.148805 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 99 nu = 0.092663 obj = -1.314353, rho = -0.190669 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 194 nu = 0.068592 obj = -1.359597, rho = -0.186735 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 190 nu = 0.050186 obj = -1.394371, rho = -0.212135 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..* optimization finished, #iter = 278 nu = 0.035643 obj = -1.398696, rho = -0.247720 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..* optimization finished, #iter = 278 nu = 0.024779 obj = -1.398696, rho = -0.247720 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..* optimization finished, #iter = 278 nu = 0.017226 obj = -1.398696, rho = -0.247720 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..* optimization finished, #iter = 278 nu = 0.011975 obj = -1.398696, rho = -0.247720 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..* optimization finished, #iter = 278 nu = 0.008325 obj = -1.398696, rho = -0.247720 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..* optimization finished, #iter = 278 nu = 0.005788 obj = -1.398696, rho = -0.247720 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..* optimization finished, #iter = 278 nu = 0.004023 obj = -1.398696, rho = -0.247720 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..* optimization finished, #iter = 278 nu = 0.002797 obj = -1.398696, rho = -0.247720 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 53 nu = 0.564212 obj = -0.382519, rho = -0.207710 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 35 nu = 0.479817 obj = -0.462838, rho = -0.185769 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 54 nu = 0.403939 obj = -0.556472, rho = -0.145942 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 52 nu = 0.336997 obj = -0.669071, rho = -0.063910 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 57 nu = 0.283254 obj = -0.803715, rho = -0.008184 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 83 nu = 0.237397 obj = -0.957259, rho = 0.059069 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 93 nu = 0.193016 obj = -1.144187, rho = 0.073723 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.163074 obj = -1.360217, rho = 0.170456 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.135470 obj = -1.623285, rho = 0.185533 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 157 nu = 0.111797 obj = -1.924867, rho = 0.222808 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.092352 obj = -2.274464, rho = 0.197269 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.074864 obj = -2.693936, rho = 0.130409 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.1% (961/1000) (classification) .* optimization finished, #iter = 173 nu = 0.062924 obj = -3.220552, rho = 0.149091 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 95.9% (959/1000) (classification) .*..........................* optimization finished, #iter = 2787 nu = 0.051054 obj = -3.802410, rho = 0.114136 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) .*.* optimization finished, #iter = 278 nu = 0.041980 obj = -4.555823, rho = 0.043941 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 168 nu = 0.035001 obj = -5.489590, rho = 0.220684 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 166 nu = 0.030232 obj = -6.553251, rho = 0.490228 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) .* optimization finished, #iter = 177 nu = 0.025559 obj = -7.598540, rho = 0.724124 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.1% (951/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.023339 obj = -8.381509, rho = 1.008772 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 93.9% (939/1000) (classification) .* optimization finished, #iter = 144 nu = 0.016779 obj = -8.391563, rho = 1.045606 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 93.9% (939/1000) (classification) * optimization finished, #iter = 44 nu = 0.561916 obj = -0.381028, rho = -0.126299 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 57 nu = 0.473536 obj = -0.462024, rho = -0.156932 nSV = 51, nBSV = 44 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 44 nu = 0.401693 obj = -0.557818, rho = -0.111769 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 68 nu = 0.343919 obj = -0.665523, rho = -0.087247 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 76 nu = 0.283786 obj = -0.786085, rho = -0.076924 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 53 nu = 0.233330 obj = -0.924178, rho = 0.001258 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.196710 obj = -1.079624, rho = -0.043865 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.156057 obj = -1.239277, rho = -0.059948 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 65 nu = 0.127340 obj = -1.429142, rho = -0.004082 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 172 nu = 0.105614 obj = -1.586313, rho = 0.017187 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *..* optimization finished, #iter = 269 nu = 0.081272 obj = -1.722872, rho = -0.081068 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.062311 obj = -1.843013, rho = -0.179887 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 81 nu = 0.047958 obj = -1.932500, rho = -0.167561 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..* optimization finished, #iter = 242 nu = 0.034475 obj = -1.945947, rho = -0.130654 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..* optimization finished, #iter = 247 nu = 0.023967 obj = -1.945947, rho = -0.130724 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..* optimization finished, #iter = 247 nu = 0.016662 obj = -1.945947, rho = -0.130724 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..* optimization finished, #iter = 247 nu = 0.011583 obj = -1.945947, rho = -0.130724 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..* optimization finished, #iter = 247 nu = 0.008053 obj = -1.945947, rho = -0.130724 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..* optimization finished, #iter = 247 nu = 0.005598 obj = -1.945947, rho = -0.130724 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..* optimization finished, #iter = 247 nu = 0.003892 obj = -1.945947, rho = -0.130724 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 40 nu = 0.601925 obj = -0.406265, rho = -0.258710 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 36 nu = 0.502713 obj = -0.491299, rho = -0.254332 nSV = 53, nBSV = 49 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 40 nu = 0.421244 obj = -0.592538, rho = -0.302015 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 28 nu = 0.363465 obj = -0.713222, rho = -0.170094 nSV = 39, nBSV = 35 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 76 nu = 0.298823 obj = -0.845772, rho = -0.179799 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 64 nu = 0.244102 obj = -1.013774, rho = -0.138200 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 78 nu = 0.204668 obj = -1.225864, rho = -0.173838 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 84 nu = 0.172395 obj = -1.479963, rho = -0.252415 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.155800 obj = -1.741392, rho = -0.378305 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) ....*..* optimization finished, #iter = 626 nu = 0.127255 obj = -1.943145, rho = -0.375384 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 143 nu = 0.095625 obj = -2.174093, rho = -0.380478 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 260 nu = 0.075844 obj = -2.443879, rho = -0.390586 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 91 nu = 0.059414 obj = -2.752263, rho = -0.380447 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 78 nu = 0.048714 obj = -2.968665, rho = -0.341686 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 69 nu = 0.037092 obj = -3.089886, rho = -0.644101 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 74 nu = 0.026521 obj = -3.096779, rho = -0.737778 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 74 nu = 0.018437 obj = -3.096779, rho = -0.737778 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 74 nu = 0.012817 obj = -3.096779, rho = -0.737778 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 74 nu = 0.008911 obj = -3.096779, rho = -0.737778 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 74 nu = 0.006195 obj = -3.096779, rho = -0.737778 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 36 nu = 0.660000 obj = -0.448585, rho = -0.146909 nSV = 68, nBSV = 65 Total nSV = 68 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 44 nu = 0.560477 obj = -0.540913, rho = -0.109039 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 96% (96/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 43 nu = 0.469496 obj = -0.651070, rho = -0.082427 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 43 nu = 0.387841 obj = -0.785471, rho = -0.084675 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 93 nu = 0.325308 obj = -0.951019, rho = -0.143553 nSV = 37, nBSV = 28 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.270491 obj = -1.162356, rho = -0.095633 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 67 nu = 0.229279 obj = -1.434383, rho = -0.076588 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 89 nu = 0.196604 obj = -1.775612, rho = -0.015505 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 75 nu = 0.166320 obj = -2.215948, rho = -0.066637 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.151120 obj = -2.750791, rho = 0.042705 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.127812 obj = -3.351811, rho = -0.141721 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 86 nu = 0.107982 obj = -4.117193, rho = -0.071618 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 171 nu = 0.098928 obj = -4.971054, rho = -0.200794 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.082125 obj = -5.835022, rho = -0.282892 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 230 nu = 0.069494 obj = -6.565505, rho = -0.274641 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 158 nu = 0.053540 obj = -7.301135, rho = -0.235230 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 186 nu = 0.045517 obj = -7.769023, rho = -0.711241 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 225 nu = 0.032153 obj = -7.771508, rho = -0.758545 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 225 nu = 0.022353 obj = -7.771508, rho = -0.758545 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 225 nu = 0.015539 obj = -7.771508, rho = -0.758545 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 61 nu = 0.609831 obj = -0.427619, rho = -0.166205 nSV = 65, nBSV = 57 Total nSV = 65 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 26 nu = 0.518433 obj = -0.528779, rho = -0.154643 nSV = 52, nBSV = 50 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 30 nu = 0.455281 obj = -0.649217, rho = -0.212300 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 34 nu = 0.384220 obj = -0.797048, rho = -0.199437 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 30 nu = 0.334400 obj = -0.972479, rho = -0.358554 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 50 nu = 0.280079 obj = -1.176331, rho = -0.446568 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 175 nu = 0.238703 obj = -1.420729, rho = -0.537445 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.198129 obj = -1.713389, rho = -0.540158 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 140 nu = 0.163178 obj = -2.098329, rho = -0.535572 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 82 nu = 0.141161 obj = -2.588021, rho = -0.550699 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.124170 obj = -3.135644, rho = -0.585245 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 61 nu = 0.102218 obj = -3.796940, rho = -0.779976 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.096343 obj = -4.446707, rho = -0.996716 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 222 nu = 0.079301 obj = -4.764335, rho = -1.374804 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 259 nu = 0.057455 obj = -4.949821, rho = -1.424657 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.043107 obj = -5.034586, rho = -1.461678 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.029968 obj = -5.034586, rho = -1.461678 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.020833 obj = -5.034586, rho = -1.461678 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.014483 obj = -5.034586, rho = -1.461678 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.010069 obj = -5.034586, rho = -1.461678 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 50 nu = 0.543741 obj = -0.357844, rho = -0.002094 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 38 nu = 0.454877 obj = -0.422807, rho = 0.030684 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 49 nu = 0.379095 obj = -0.492191, rho = -0.087067 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *..* optimization finished, #iter = 203 nu = 0.304225 obj = -0.573252, rho = -0.121335 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 56 nu = 0.247285 obj = -0.670009, rho = -0.104680 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 58 nu = 0.198862 obj = -0.779653, rho = -0.125018 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 64 nu = 0.161474 obj = -0.911937, rho = -0.079430 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 88 nu = 0.130549 obj = -1.066861, rho = -0.051461 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 31 nu = 0.106633 obj = -1.261787, rho = -0.130888 nSV = 14, nBSV = 9 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 63 nu = 0.088102 obj = -1.486271, rho = -0.071679 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.072737 obj = -1.729789, rho = -0.171445 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 62 nu = 0.060225 obj = -1.989924, rho = -0.316458 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 47 nu = 0.049564 obj = -2.230055, rho = -0.347622 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 78 nu = 0.039931 obj = -2.366807, rho = -0.335820 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.029569 obj = -2.401209, rho = -0.247976 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.020556 obj = -2.401209, rho = -0.247976 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.014290 obj = -2.401209, rho = -0.247976 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.009935 obj = -2.401209, rho = -0.247976 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.006906 obj = -2.401209, rho = -0.247976 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.004801 obj = -2.401209, rho = -0.247976 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 46 nu = 0.605875 obj = -0.432305, rho = 0.007821 nSV = 65, nBSV = 59 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 55 nu = 0.519801 obj = -0.539120, rho = -0.083124 nSV = 56, nBSV = 48 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 38 nu = 0.448754 obj = -0.676146, rho = -0.095338 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 52 nu = 0.412077 obj = -0.829830, rho = -0.009928 nSV = 43, nBSV = 39 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.344234 obj = -1.004122, rho = -0.066033 nSV = 39, nBSV = 30 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *..* optimization finished, #iter = 219 nu = 0.286626 obj = -1.221953, rho = -0.135378 nSV = 34, nBSV = 25 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 58 nu = 0.243697 obj = -1.494862, rho = -0.175055 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 138 nu = 0.209759 obj = -1.822904, rho = -0.036769 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.173420 obj = -2.219631, rho = 0.017363 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 60 nu = 0.154750 obj = -2.687147, rho = -0.096889 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 85 nu = 0.134046 obj = -3.131577, rho = -0.115311 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*.* optimization finished, #iter = 361 nu = 0.105452 obj = -3.599498, rho = -0.117817 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 161 nu = 0.082615 obj = -4.226512, rho = -0.179155 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 165 nu = 0.070284 obj = -4.959725, rho = -0.166461 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 146 nu = 0.061056 obj = -5.612436, rho = 0.137961 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 160 nu = 0.049741 obj = -5.808658, rho = 0.141731 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 160 nu = 0.034580 obj = -5.808658, rho = 0.141731 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 160 nu = 0.024040 obj = -5.808658, rho = 0.141731 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 160 nu = 0.016712 obj = -5.808658, rho = 0.141731 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 160 nu = 0.011618 obj = -5.808658, rho = 0.141731 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 69 nu = 0.592717 obj = -0.400847, rho = -0.206130 nSV = 62, nBSV = 55 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 37 nu = 0.495923 obj = -0.486766, rho = -0.167240 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 58 nu = 0.424628 obj = -0.586871, rho = -0.090385 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 54 nu = 0.358079 obj = -0.702767, rho = -0.127041 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 66 nu = 0.292010 obj = -0.844395, rho = -0.138076 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 63 nu = 0.240938 obj = -1.030277, rho = -0.184201 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 57 nu = 0.205025 obj = -1.261346, rho = -0.360914 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 80 nu = 0.175560 obj = -1.540096, rho = -0.338368 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 58 nu = 0.148709 obj = -1.887463, rho = -0.359020 nSV = 18, nBSV = 13 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 80 nu = 0.123797 obj = -2.317325, rho = -0.393232 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 136 nu = 0.108343 obj = -2.850927, rho = -0.393594 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 133 nu = 0.094074 obj = -3.479434, rho = -0.305142 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 169 nu = 0.077167 obj = -4.219463, rho = -0.249082 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.066214 obj = -5.221253, rho = -0.187534 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) ..*.....* optimization finished, #iter = 787 nu = 0.058828 obj = -6.265927, rho = -0.072184 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) ...*...* optimization finished, #iter = 624 nu = 0.052339 obj = -7.333462, rho = 0.005581 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) ....*...* optimization finished, #iter = 787 nu = 0.044713 obj = -7.877860, rho = 0.108021 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) ...*..* optimization finished, #iter = 526 nu = 0.032872 obj = -7.943088, rho = 0.144656 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) ...*..* optimization finished, #iter = 526 nu = 0.022852 obj = -7.943088, rho = 0.144656 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) ...*..* optimization finished, #iter = 526 nu = 0.015887 obj = -7.943088, rho = 0.144656 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 37 nu = 0.676619 obj = -0.469940, rho = -0.312466 nSV = 68, nBSV = 66 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 75 nu = 0.579610 obj = -0.571516, rho = -0.451576 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.486694 obj = -0.698034, rho = -0.492497 nSV = 53, nBSV = 46 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 43 nu = 0.430818 obj = -0.843498, rho = -0.471438 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 56 nu = 0.357090 obj = -1.009742, rho = -0.479879 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 76 nu = 0.294472 obj = -1.199022, rho = -0.493979 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 189 nu = 0.244368 obj = -1.417966, rho = -0.501456 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 67 nu = 0.202548 obj = -1.684311, rho = -0.476904 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 75 nu = 0.173058 obj = -1.987770, rho = -0.598259 nSV = 19, nBSV = 15 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *....* optimization finished, #iter = 407 nu = 0.139613 obj = -2.285630, rho = -0.608526 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.109643 obj = -2.652242, rho = -0.615952 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.087034 obj = -3.139056, rho = -0.633844 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *....* optimization finished, #iter = 424 nu = 0.070923 obj = -3.747128, rho = -0.741603 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) ..*.* optimization finished, #iter = 373 nu = 0.058718 obj = -4.545750, rho = -0.811082 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*..* optimization finished, #iter = 382 nu = 0.049223 obj = -5.528887, rho = -0.839144 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 224 nu = 0.043260 obj = -6.688370, rho = -1.078682 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 231 nu = 0.035855 obj = -7.937130, rho = -1.197371 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.034289 obj = -8.997214, rho = -1.717583 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 222 nu = 0.026091 obj = -9.071151, rho = -1.896694 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 222 nu = 0.018138 obj = -9.071151, rho = -1.896694 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 83 nu = 0.601483 obj = -0.408574, rho = -0.261188 nSV = 64, nBSV = 58 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 53 nu = 0.497758 obj = -0.497130, rho = -0.224456 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 31 nu = 0.428872 obj = -0.609511, rho = -0.161870 nSV = 45, nBSV = 42 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 44 nu = 0.377250 obj = -0.731835, rho = -0.044297 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 50 nu = 0.308261 obj = -0.870486, rho = -0.074050 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 171 nu = 0.254572 obj = -1.041900, rho = -0.090068 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.211474 obj = -1.248669, rho = -0.069744 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 61 nu = 0.174781 obj = -1.502703, rho = -0.107198 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 64 nu = 0.147090 obj = -1.817353, rho = -0.002741 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 150 nu = 0.127881 obj = -2.167353, rho = 0.089340 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 68 nu = 0.104741 obj = -2.545945, rho = -0.025064 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.084940 obj = -3.000397, rho = -0.090764 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 68 nu = 0.070323 obj = -3.551015, rho = 0.022661 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.063689 obj = -3.992274, rho = 0.435971 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .* optimization finished, #iter = 123 nu = 0.047311 obj = -4.290134, rho = 0.647635 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .* optimization finished, #iter = 198 nu = 0.034581 obj = -4.683019, rho = 0.646624 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .* optimization finished, #iter = 147 nu = 0.029892 obj = -5.022408, rho = 1.186464 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) .* optimization finished, #iter = 147 nu = 0.020781 obj = -5.022408, rho = 1.186464 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) .* optimization finished, #iter = 147 nu = 0.014446 obj = -5.022408, rho = 1.186464 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) .* optimization finished, #iter = 147 nu = 0.010043 obj = -5.022408, rho = 1.186464 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 36 nu = 0.559229 obj = -0.366330, rho = -0.141526 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 58 nu = 0.453209 obj = -0.437389, rho = -0.171516 nSV = 52, nBSV = 44 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 26 nu = 0.380351 obj = -0.524756, rho = -0.147779 nSV = 40, nBSV = 36 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 66 nu = 0.325549 obj = -0.624600, rho = -0.235757 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 91 nu = 0.264325 obj = -0.739326, rho = -0.253665 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 81 nu = 0.216173 obj = -0.876180, rho = -0.216339 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.180718 obj = -1.034755, rho = -0.234351 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.145237 obj = -1.233557, rho = -0.286991 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.118214 obj = -1.490244, rho = -0.241846 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 38 nu = 0.103427 obj = -1.823726, rho = -0.134099 nSV = 13, nBSV = 9 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 79 nu = 0.090993 obj = -2.131341, rho = -0.141188 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 79 nu = 0.070884 obj = -2.472450, rho = -0.111608 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 70 nu = 0.060312 obj = -2.858950, rho = -0.361726 nSV = 9, nBSV = 3 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 81 nu = 0.052834 obj = -3.069772, rho = -0.927405 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 90 nu = 0.037865 obj = -3.074947, rho = -0.999445 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 90 nu = 0.026323 obj = -3.074947, rho = -0.999445 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 90 nu = 0.018300 obj = -3.074947, rho = -0.999445 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 90 nu = 0.012722 obj = -3.074947, rho = -0.999445 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 90 nu = 0.008844 obj = -3.074947, rho = -0.999445 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 90 nu = 0.006148 obj = -3.074947, rho = -0.999445 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 38 nu = 0.582628 obj = -0.392312, rho = -0.322399 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 34 nu = 0.491749 obj = -0.474188, rho = -0.324922 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 53 nu = 0.409503 obj = -0.568883, rho = -0.313422 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 51 nu = 0.340372 obj = -0.686575, rho = -0.298865 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 64 nu = 0.280000 obj = -0.836541, rho = -0.327832 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 67 nu = 0.240616 obj = -1.028663, rho = -0.309336 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 79 nu = 0.200785 obj = -1.273691, rho = -0.292513 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 72 nu = 0.173496 obj = -1.573612, rho = -0.309576 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 61 nu = 0.150403 obj = -1.948954, rho = -0.331841 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 87 nu = 0.127743 obj = -2.412960, rho = -0.336596 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 62 nu = 0.110761 obj = -3.009876, rho = -0.487913 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 77 nu = 0.096130 obj = -3.727055, rho = -0.617172 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 163 nu = 0.080219 obj = -4.669906, rho = -0.560337 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 166 nu = 0.069957 obj = -5.969471, rho = -0.650421 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 179 nu = 0.066286 obj = -7.501709, rho = -0.891243 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 96% (960/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.057696 obj = -9.119155, rho = -1.042709 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 95.4% (954/1000) (classification) .*.* optimization finished, #iter = 214 nu = 0.051039 obj = -10.881998, rho = -1.178034 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 95.3% (953/1000) (classification) .*.* optimization finished, #iter = 239 nu = 0.043144 obj = -12.362184, rho = -1.281180 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.6% (946/1000) (classification) ..*..* optimization finished, #iter = 429 nu = 0.034746 obj = -13.557510, rho = -1.321114 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 93.9% (939/1000) (classification) ...*..* optimization finished, #iter = 521 nu = 0.025468 obj = -14.850062, rho = -1.321131 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 93.9% (939/1000) (classification) * optimization finished, #iter = 47 nu = 0.626529 obj = -0.425986, rho = -0.050046 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 64 nu = 0.539213 obj = -0.514880, rho = -0.039214 nSV = 57, nBSV = 50 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 48 nu = 0.453414 obj = -0.616531, rho = 0.038114 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 46 nu = 0.378843 obj = -0.727576, rho = 0.013469 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.308060 obj = -0.857948, rho = 0.030572 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 56 nu = 0.256621 obj = -1.011214, rho = 0.009650 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.207569 obj = -1.177231, rho = 0.025638 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 74 nu = 0.165084 obj = -1.394153, rho = 0.037432 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 45 nu = 0.137095 obj = -1.671441, rho = 0.042009 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 44 nu = 0.118124 obj = -1.975876, rho = 0.079983 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 71 nu = 0.099697 obj = -2.273184, rho = 0.200036 nSV = 12, nBSV = 7 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.079497 obj = -2.534146, rho = 0.317192 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 142 nu = 0.067124 obj = -2.704839, rho = 0.310456 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .....*...* optimization finished, #iter = 808 nu = 0.048204 obj = -2.720751, rho = 0.234376 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .....*...* optimization finished, #iter = 808 nu = 0.033511 obj = -2.720751, rho = 0.234376 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .....*...* optimization finished, #iter = 808 nu = 0.023297 obj = -2.720751, rho = 0.234376 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .....*...* optimization finished, #iter = 808 nu = 0.016196 obj = -2.720751, rho = 0.234376 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .....*...* optimization finished, #iter = 808 nu = 0.011259 obj = -2.720751, rho = 0.234376 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .....*...* optimization finished, #iter = 808 nu = 0.007827 obj = -2.720751, rho = 0.234376 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .....*...* optimization finished, #iter = 808 nu = 0.005441 obj = -2.720751, rho = 0.234376 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 39 nu = 0.604864 obj = -0.416768, rho = -0.154754 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 33 nu = 0.520000 obj = -0.507801, rho = -0.144108 nSV = 53, nBSV = 50 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 35 nu = 0.441049 obj = -0.612909, rho = -0.206492 nSV = 47, nBSV = 43 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 54 nu = 0.372691 obj = -0.734430, rho = -0.201058 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.309750 obj = -0.875891, rho = -0.209561 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.257559 obj = -1.048532, rho = -0.231639 nSV = 27, nBSV = 22 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 80 nu = 0.213281 obj = -1.254757, rho = -0.285423 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.176831 obj = -1.496587, rho = -0.348556 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 216 nu = 0.147010 obj = -1.794932, rho = -0.362479 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 83 nu = 0.122341 obj = -2.158896, rho = -0.335485 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 47 nu = 0.104653 obj = -2.594481, rho = -0.219585 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.088635 obj = -3.009910, rho = -0.157834 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 160 nu = 0.074321 obj = -3.452772, rho = -0.261574 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) ......* optimization finished, #iter = 691 nu = 0.059905 obj = -3.822476, rho = -0.338450 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 259 nu = 0.045587 obj = -4.176412, rho = -0.358120 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 278 nu = 0.033720 obj = -4.554076, rho = -0.357664 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.028551 obj = -4.795818, rho = -0.195709 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.019849 obj = -4.795818, rho = -0.195709 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.013799 obj = -4.795818, rho = -0.195709 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.009593 obj = -4.795818, rho = -0.195709 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 36 nu = 0.539591 obj = -0.376575, rho = -0.155757 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 39 nu = 0.470463 obj = -0.459800, rho = -0.150689 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 40 nu = 0.399170 obj = -0.557089, rho = -0.075844 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 51 nu = 0.331303 obj = -0.676750, rho = -0.101776 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 47 nu = 0.284284 obj = -0.823144, rho = -0.099762 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 42 nu = 0.239955 obj = -0.993170, rho = -0.083067 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 49 nu = 0.201766 obj = -1.193003, rho = 0.011263 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.166043 obj = -1.438249, rho = -0.030920 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 81 nu = 0.139328 obj = -1.756592, rho = -0.001288 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *......* optimization finished, #iter = 671 nu = 0.118023 obj = -2.128328, rho = 0.004699 nSV = 18, nBSV = 7 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 189 nu = 0.095396 obj = -2.629530, rho = 0.006499 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 176 nu = 0.081873 obj = -3.334922, rho = 0.066344 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 78 nu = 0.071662 obj = -4.273456, rho = 0.167077 nSV = 11, nBSV = 5 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *..* optimization finished, #iter = 220 nu = 0.067132 obj = -5.386813, rho = 0.174770 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..* optimization finished, #iter = 278 nu = 0.058977 obj = -6.589895, rho = 0.344001 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 93 nu = 0.052362 obj = -7.981332, rho = 0.772060 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 157 nu = 0.049741 obj = -8.971792, rho = 1.395373 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.037381 obj = -9.034027, rho = 1.589781 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.025987 obj = -9.034027, rho = 1.589781 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.018066 obj = -9.034027, rho = 1.589781 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 38 nu = 0.520306 obj = -0.338975, rho = -0.143289 nSV = 54, nBSV = 51 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 64 nu = 0.432580 obj = -0.396156, rho = -0.113502 nSV = 47, nBSV = 40 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 42 nu = 0.351663 obj = -0.460739, rho = -0.133815 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 66 nu = 0.287764 obj = -0.534402, rho = -0.091539 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 67 nu = 0.238284 obj = -0.614825, rho = -0.176953 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.185058 obj = -0.696800, rho = -0.212876 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.145878 obj = -0.798583, rho = -0.253639 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 71 nu = 0.115269 obj = -0.923423, rho = -0.265664 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 25 nu = 0.094954 obj = -1.076528, rho = -0.355825 nSV = 11, nBSV = 7 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 32 nu = 0.079477 obj = -1.207855, rho = -0.504954 nSV = 11, nBSV = 5 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 55 nu = 0.062616 obj = -1.326600, rho = -0.575237 nSV = 10, nBSV = 4 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.049899 obj = -1.361143, rho = -0.527264 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.034689 obj = -1.361143, rho = -0.527264 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.024116 obj = -1.361143, rho = -0.527264 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.016765 obj = -1.361143, rho = -0.527264 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.011655 obj = -1.361143, rho = -0.527264 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.008102 obj = -1.361143, rho = -0.527264 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.005633 obj = -1.361143, rho = -0.527264 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.003916 obj = -1.361143, rho = -0.527264 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.002722 obj = -1.361143, rho = -0.527264 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 37 nu = 0.595949 obj = -0.390569, rho = -0.121992 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 38 nu = 0.504515 obj = -0.460949, rho = -0.185091 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.410153 obj = -0.538988, rho = -0.198351 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 55 nu = 0.334758 obj = -0.627416, rho = -0.191470 nSV = 38, nBSV = 29 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 38 nu = 0.270124 obj = -0.725935, rho = -0.182160 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 50 nu = 0.221169 obj = -0.838054, rho = -0.117988 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.178606 obj = -0.956579, rho = -0.094055 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 91 nu = 0.143153 obj = -1.078689, rho = -0.063856 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 282 nu = 0.113026 obj = -1.194805, rho = -0.132046 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*......* optimization finished, #iter = 723 nu = 0.085889 obj = -1.309985, rho = -0.108238 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 183 nu = 0.066655 obj = -1.442961, rho = -0.054354 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 270 nu = 0.051652 obj = -1.568695, rho = 0.014013 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.039803 obj = -1.665644, rho = 0.010130 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 67 nu = 0.030034 obj = -1.695315, rho = -0.021643 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 67 nu = 0.020879 obj = -1.695315, rho = -0.021643 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 67 nu = 0.014515 obj = -1.695315, rho = -0.021643 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 67 nu = 0.010091 obj = -1.695315, rho = -0.021643 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 67 nu = 0.007015 obj = -1.695315, rho = -0.021643 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 67 nu = 0.004877 obj = -1.695315, rho = -0.021643 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 67 nu = 0.003390 obj = -1.695315, rho = -0.021643 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 48 nu = 0.583286 obj = -0.396656, rho = -0.304649 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 35 nu = 0.496491 obj = -0.480845, rho = -0.276432 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 42 nu = 0.432867 obj = -0.571676, rho = -0.203411 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 52 nu = 0.352327 obj = -0.670156, rho = -0.178966 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *..* optimization finished, #iter = 206 nu = 0.287169 obj = -0.782516, rho = -0.150834 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 170 nu = 0.230902 obj = -0.913927, rho = -0.116066 nSV = 29, nBSV = 19 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 51 nu = 0.190144 obj = -1.078126, rho = -0.197333 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 98 nu = 0.163665 obj = -1.243918, rho = -0.267165 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *...* optimization finished, #iter = 393 nu = 0.131716 obj = -1.373800, rho = -0.376358 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ....* optimization finished, #iter = 447 nu = 0.102463 obj = -1.488842, rho = -0.305868 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ...* optimization finished, #iter = 351 nu = 0.074987 obj = -1.602167, rho = -0.298969 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 246 nu = 0.056468 obj = -1.728108, rho = -0.293576 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 74 nu = 0.045612 obj = -1.823594, rho = -0.425894 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 94 nu = 0.032439 obj = -1.830777, rho = -0.511060 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 94 nu = 0.022551 obj = -1.830777, rho = -0.511060 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 94 nu = 0.015677 obj = -1.830777, rho = -0.511060 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 94 nu = 0.010899 obj = -1.830777, rho = -0.511060 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 94 nu = 0.007577 obj = -1.830777, rho = -0.511060 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 94 nu = 0.005267 obj = -1.830777, rho = -0.511060 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 94 nu = 0.003662 obj = -1.830777, rho = -0.511060 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 34 nu = 0.550402 obj = -0.362224, rho = -0.083317 nSV = 57, nBSV = 54 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 42 nu = 0.467307 obj = -0.425701, rho = -0.177799 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 44 nu = 0.384759 obj = -0.492013, rho = -0.191489 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 77 nu = 0.302773 obj = -0.566395, rho = -0.219164 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 199 nu = 0.237999 obj = -0.664172, rho = -0.209075 nSV = 30, nBSV = 20 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 44 nu = 0.203334 obj = -0.784855, rho = -0.194724 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 86 nu = 0.164847 obj = -0.903401, rho = -0.228860 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 60 nu = 0.138636 obj = -1.023427, rho = -0.124915 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 57 nu = 0.107774 obj = -1.127688, rho = -0.167841 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *...* optimization finished, #iter = 359 nu = 0.082445 obj = -1.220550, rho = -0.257791 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 90 nu = 0.060935 obj = -1.334971, rho = -0.297239 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 89 nu = 0.045624 obj = -1.486574, rho = -0.325121 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 81 nu = 0.036194 obj = -1.673044, rho = -0.344893 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 54 nu = 0.031119 obj = -1.801159, rho = -0.199458 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 93 nu = 0.022206 obj = -1.803007, rho = -0.176660 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 93 nu = 0.015437 obj = -1.803007, rho = -0.176660 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 93 nu = 0.010732 obj = -1.803007, rho = -0.176660 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 93 nu = 0.007461 obj = -1.803007, rho = -0.176660 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 93 nu = 0.005187 obj = -1.803007, rho = -0.176660 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 93 nu = 0.003606 obj = -1.803007, rho = -0.176660 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 40 nu = 0.563469 obj = -0.388796, rho = -0.144132 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 32 nu = 0.480000 obj = -0.474150, rho = -0.123740 nSV = 49, nBSV = 46 Total nSV = 49 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 37 nu = 0.405460 obj = -0.578195, rho = -0.114036 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 32 nu = 0.347745 obj = -0.704890, rho = -0.110657 nSV = 36, nBSV = 33 Total nSV = 36 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 54 nu = 0.289710 obj = -0.857459, rho = -0.111344 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 48 nu = 0.245924 obj = -1.048964, rho = -0.081823 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 68 nu = 0.206780 obj = -1.290665, rho = -0.043512 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 79 nu = 0.175746 obj = -1.600031, rho = -0.004402 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 90 nu = 0.152795 obj = -1.986921, rho = 0.033637 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 77 nu = 0.136495 obj = -2.416463, rho = 0.070800 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 98 nu = 0.120680 obj = -2.873629, rho = 0.059767 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 128 nu = 0.102141 obj = -3.255020, rho = 0.057106 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) ....*....* optimization finished, #iter = 879 nu = 0.078099 obj = -3.592484, rho = 0.061296 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.059731 obj = -4.020925, rho = 0.083303 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 98 nu = 0.052097 obj = -4.381750, rho = 0.215909 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*...* optimization finished, #iter = 447 nu = 0.037596 obj = -4.390477, rho = 0.217302 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*...* optimization finished, #iter = 447 nu = 0.026137 obj = -4.390477, rho = 0.217302 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*...* optimization finished, #iter = 447 nu = 0.018170 obj = -4.390477, rho = 0.217302 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*...* optimization finished, #iter = 447 nu = 0.012632 obj = -4.390477, rho = 0.217302 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*...* optimization finished, #iter = 447 nu = 0.008781 obj = -4.390477, rho = 0.217302 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 33 nu = 0.659074 obj = -0.472733, rho = -0.076296 nSV = 66, nBSV = 64 Total nSV = 66 Accuracy = 96% (96/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 42 nu = 0.576380 obj = -0.587119, rho = -0.129976 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 96% (96/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 35 nu = 0.495800 obj = -0.728788, rho = -0.155129 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 36 nu = 0.429257 obj = -0.905485, rho = -0.153598 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.367775 obj = -1.128969, rho = -0.131475 nSV = 38, nBSV = 33 Total nSV = 38 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 55 nu = 0.310332 obj = -1.417247, rho = -0.100085 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.272598 obj = -1.795623, rho = -0.081830 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.237697 obj = -2.287106, rho = -0.115264 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 99 nu = 0.213979 obj = -2.916877, rho = -0.184251 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 175 nu = 0.190268 obj = -3.679830, rho = -0.044414 nSV = 25, nBSV = 14 Total nSV = 25 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 203 nu = 0.166713 obj = -4.664092, rho = 0.004437 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*..* optimization finished, #iter = 378 nu = 0.148010 obj = -5.909686, rho = 0.004233 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 254 nu = 0.129834 obj = -7.399103, rho = 0.007407 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) ...* optimization finished, #iter = 366 nu = 0.112346 obj = -9.349995, rho = -0.084042 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*........................* optimization finished, #iter = 2622 nu = 0.098238 obj = -11.752340, rho = -0.139053 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..*.* optimization finished, #iter = 306 nu = 0.085678 obj = -14.969994, rho = -0.078337 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..* optimization finished, #iter = 271 nu = 0.073985 obj = -19.218135, rho = 0.080522 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..*...* optimization finished, #iter = 505 nu = 0.065370 obj = -25.009848, rho = 0.247600 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) .....*.* optimization finished, #iter = 639 nu = 0.063027 obj = -32.218741, rho = 0.918396 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .....*..* optimization finished, #iter = 766 nu = 0.058214 obj = -40.046526, rho = 1.364454 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 67 nu = 0.630153 obj = -0.451386, rho = -0.435739 nSV = 66, nBSV = 59 Total nSV = 66 Accuracy = 96% (96/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 41 nu = 0.549062 obj = -0.560703, rho = -0.418792 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 60 nu = 0.476630 obj = -0.692944, rho = -0.436949 nSV = 50, nBSV = 43 Total nSV = 50 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 47 nu = 0.407264 obj = -0.858519, rho = -0.570359 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 96% (96/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 58 nu = 0.346147 obj = -1.068035, rho = -0.574637 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 96% (96/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 64 nu = 0.297728 obj = -1.331027, rho = -0.702633 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 96% (96/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 89 nu = 0.258072 obj = -1.678509, rho = -0.706242 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 96% (96/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.224715 obj = -2.118993, rho = -0.790475 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 97% (97/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 72 nu = 0.202457 obj = -2.675313, rho = -0.983896 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 69 nu = 0.175383 obj = -3.348847, rho = -0.907751 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.155571 obj = -4.164348, rho = -0.882719 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 80 nu = 0.129710 obj = -5.193586, rho = -0.896870 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.111790 obj = -6.598376, rho = -0.955430 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.104505 obj = -8.285520, rho = -1.043584 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 161 nu = 0.095996 obj = -10.030760, rho = -1.474193 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) ...*.*..* optimization finished, #iter = 546 nu = 0.084622 obj = -11.359194, rho = -1.852728 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) ..*.* optimization finished, #iter = 314 nu = 0.067389 obj = -12.283184, rho = -1.772269 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..*......* optimization finished, #iter = 819 nu = 0.052109 obj = -12.592652, rho = -1.849643 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*......* optimization finished, #iter = 819 nu = 0.036226 obj = -12.592652, rho = -1.849643 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*......* optimization finished, #iter = 819 nu = 0.025184 obj = -12.592652, rho = -1.849643 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 33 nu = 0.547890 obj = -0.366856, rho = -0.273962 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 37 nu = 0.466324 obj = -0.436587, rho = -0.269460 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 37 nu = 0.396461 obj = -0.514366, rho = -0.361253 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 186 nu = 0.319494 obj = -0.595826, rho = -0.363999 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.258382 obj = -0.687808, rho = -0.314700 nSV = 30, nBSV = 20 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 54 nu = 0.204463 obj = -0.795729, rho = -0.293326 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 40 nu = 0.168075 obj = -0.920187, rho = -0.303021 nSV = 18, nBSV = 13 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 34 nu = 0.136210 obj = -1.054643, rho = -0.274268 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.109607 obj = -1.190352, rho = -0.252946 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.087639 obj = -1.307687, rho = -0.291781 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.069338 obj = -1.394993, rho = -0.516670 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 237 nu = 0.051826 obj = -1.429435, rho = -0.685518 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 198 nu = 0.036544 obj = -1.434110, rho = -0.668444 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 198 nu = 0.025405 obj = -1.434110, rho = -0.668444 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 198 nu = 0.017662 obj = -1.434110, rho = -0.668444 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 198 nu = 0.012278 obj = -1.434110, rho = -0.668444 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 198 nu = 0.008536 obj = -1.434110, rho = -0.668444 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 198 nu = 0.005934 obj = -1.434110, rho = -0.668444 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 198 nu = 0.004125 obj = -1.434110, rho = -0.668444 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 198 nu = 0.002868 obj = -1.434110, rho = -0.668444 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 33 nu = 0.560000 obj = -0.377394, rho = -0.081142 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 52 nu = 0.489368 obj = -0.446101, rho = -0.079583 nSV = 53, nBSV = 45 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 58 nu = 0.411804 obj = -0.515978, rho = -0.124758 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 82 nu = 0.322553 obj = -0.585236, rho = -0.124690 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 94 nu = 0.255314 obj = -0.670230, rho = -0.138439 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 61 nu = 0.207795 obj = -0.760683, rho = -0.077648 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 152 nu = 0.158674 obj = -0.861816, rho = -0.087980 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 74 nu = 0.125451 obj = -0.989754, rho = -0.110874 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 46 nu = 0.100596 obj = -1.142596, rho = -0.149167 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 29 nu = 0.079769 obj = -1.320524, rho = -0.087243 nSV = 10, nBSV = 5 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 43 nu = 0.065499 obj = -1.522442, rho = 0.117094 nSV = 10, nBSV = 5 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 34 nu = 0.054356 obj = -1.712322, rho = 0.219758 nSV = 9, nBSV = 3 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.045597 obj = -1.788933, rho = 0.256464 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.031698 obj = -1.788933, rho = 0.256464 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.022037 obj = -1.788933, rho = 0.256464 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.015320 obj = -1.788933, rho = 0.256464 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.010650 obj = -1.788933, rho = 0.256464 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.007404 obj = -1.788933, rho = 0.256464 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.005147 obj = -1.788933, rho = 0.256464 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.003578 obj = -1.788933, rho = 0.256464 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 43 nu = 0.599514 obj = -0.415183, rho = -0.018369 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 84 nu = 0.518563 obj = -0.503527, rho = 0.102474 nSV = 55, nBSV = 47 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 66 nu = 0.440631 obj = -0.603819, rho = 0.151098 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 76 nu = 0.365611 obj = -0.717869, rho = 0.167402 nSV = 41, nBSV = 33 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 40 nu = 0.301181 obj = -0.861823, rho = 0.114846 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 53 nu = 0.253503 obj = -1.034658, rho = 0.045584 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 40 nu = 0.217193 obj = -1.225998, rho = 0.071831 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 49 nu = 0.174753 obj = -1.437681, rho = 0.084040 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 179 nu = 0.142904 obj = -1.694116, rho = 0.053529 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.115396 obj = -2.008573, rho = 0.031222 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 68 nu = 0.094775 obj = -2.414565, rho = -0.025537 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.079070 obj = -2.919683, rho = -0.033581 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.066877 obj = -3.551100, rho = 0.145842 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 251 nu = 0.057375 obj = -4.243074, rho = 0.288535 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 179 nu = 0.047065 obj = -5.071109, rho = 0.376791 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 96.1% (961/1000) (classification) ..* optimization finished, #iter = 269 nu = 0.040033 obj = -6.035535, rho = 0.558478 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 95% (950/1000) (classification) ....*..* optimization finished, #iter = 662 nu = 0.034481 obj = -7.011311, rho = 0.787778 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94% (940/1000) (classification) ..*.............* optimization finished, #iter = 1577 nu = 0.031038 obj = -7.500076, rho = 1.224080 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 93.7% (937/1000) (classification) ..*.............* optimization finished, #iter = 1577 nu = 0.021577 obj = -7.500076, rho = 1.224080 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 93.7% (937/1000) (classification) ..*.............* optimization finished, #iter = 1577 nu = 0.015000 obj = -7.500076, rho = 1.224080 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 93.7% (937/1000) (classification) * optimization finished, #iter = 46 nu = 0.559627 obj = -0.361499, rho = -0.250437 nSV = 57, nBSV = 54 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 69 nu = 0.459087 obj = -0.426600, rho = -0.194604 nSV = 49, nBSV = 41 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 67 nu = 0.376249 obj = -0.504739, rho = -0.200467 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 87 nu = 0.306378 obj = -0.597038, rho = -0.187865 nSV = 35, nBSV = 26 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.247860 obj = -0.713457, rho = -0.188506 nSV = 30, nBSV = 20 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.205612 obj = -0.867693, rho = -0.190746 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 68 nu = 0.171603 obj = -1.058389, rho = -0.111908 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 87 nu = 0.151396 obj = -1.286192, rho = -0.009396 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.125058 obj = -1.546920, rho = 0.022127 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.104391 obj = -1.874295, rho = 0.088009 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *..* optimization finished, #iter = 202 nu = 0.085917 obj = -2.287921, rho = 0.090653 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.077603 obj = -2.780743, rho = -0.033486 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 95 nu = 0.070179 obj = -3.207275, rho = -0.146851 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*...* optimization finished, #iter = 426 nu = 0.055325 obj = -3.442298, rho = -0.222428 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 179 nu = 0.041272 obj = -3.633436, rho = -0.267913 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 164 nu = 0.030141 obj = -3.866963, rho = -0.275237 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*..* optimization finished, #iter = 318 nu = 0.024041 obj = -4.039039, rho = -0.362933 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*..* optimization finished, #iter = 318 nu = 0.016713 obj = -4.039039, rho = -0.362933 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*..* optimization finished, #iter = 318 nu = 0.011619 obj = -4.039039, rho = -0.362933 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*..* optimization finished, #iter = 318 nu = 0.008077 obj = -4.039039, rho = -0.362933 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 31 nu = 0.581307 obj = -0.390447, rho = -0.300677 nSV = 60, nBSV = 57 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.495093 obj = -0.466804, rho = -0.292863 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 65 nu = 0.412384 obj = -0.553864, rho = -0.363056 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 70 nu = 0.331736 obj = -0.659764, rho = -0.389303 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 87 nu = 0.273756 obj = -0.793099, rho = -0.457229 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 63 nu = 0.232014 obj = -0.961524, rho = -0.415715 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.195444 obj = -1.157851, rho = -0.344251 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .* optimization finished, #iter = 149 nu = 0.159802 obj = -1.398205, rho = -0.322979 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 43 nu = 0.138448 obj = -1.704478, rho = -0.185132 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 96 nu = 0.124071 obj = -2.003181, rho = -0.031560 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 52 nu = 0.100818 obj = -2.283902, rho = -0.088385 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.081582 obj = -2.525135, rho = -0.211558 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.062647 obj = -2.744343, rho = -0.218836 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.047404 obj = -2.949354, rho = -0.296094 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.037224 obj = -3.022102, rho = -0.542171 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.025878 obj = -3.022102, rho = -0.542171 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.017990 obj = -3.022102, rho = -0.542171 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.012507 obj = -3.022102, rho = -0.542171 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.008694 obj = -3.022102, rho = -0.542171 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.006044 obj = -3.022102, rho = -0.542171 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 39 nu = 0.569002 obj = -0.383180, rho = -0.092222 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 52 nu = 0.480655 obj = -0.456850, rho = -0.185808 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 41 nu = 0.399023 obj = -0.546105, rho = -0.147228 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 46 nu = 0.333405 obj = -0.648061, rho = -0.176239 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.274893 obj = -0.763735, rho = -0.124557 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 76 nu = 0.224627 obj = -0.905861, rho = -0.162474 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 54 nu = 0.188934 obj = -1.061681, rho = -0.280118 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.155529 obj = -1.230428, rho = -0.394744 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.127892 obj = -1.416349, rho = -0.330168 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.106713 obj = -1.559377, rho = -0.113657 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 283 nu = 0.082170 obj = -1.644973, rho = -0.159399 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 231 nu = 0.059538 obj = -1.704638, rho = -0.196283 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 259 nu = 0.043799 obj = -1.740108, rho = -0.205678 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.030852 obj = -1.741539, rho = -0.177160 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.021448 obj = -1.741539, rho = -0.177160 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.014910 obj = -1.741539, rho = -0.177160 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.010366 obj = -1.741539, rho = -0.177160 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.007206 obj = -1.741539, rho = -0.177160 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.005010 obj = -1.741539, rho = -0.177160 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.003483 obj = -1.741539, rho = -0.177160 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.609952 obj = -0.397324, rho = -0.328290 nSV = 62, nBSV = 59 Total nSV = 62 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 52 nu = 0.502609 obj = -0.468133, rho = -0.405792 nSV = 55, nBSV = 47 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 59 nu = 0.409408 obj = -0.553954, rho = -0.447461 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 47 nu = 0.339534 obj = -0.656405, rho = -0.441492 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 55 nu = 0.282167 obj = -0.770625, rho = -0.430299 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.237404 obj = -0.893546, rho = -0.477533 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 67 nu = 0.185940 obj = -1.027484, rho = -0.525944 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 75 nu = 0.150389 obj = -1.185107, rho = -0.604639 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 85 nu = 0.120539 obj = -1.366650, rho = -0.661597 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.....* optimization finished, #iter = 592 nu = 0.097197 obj = -1.561162, rho = -0.621391 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 53 nu = 0.080414 obj = -1.765831, rho = -0.764971 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*...........* optimization finished, #iter = 1257 nu = 0.062904 obj = -1.932116, rho = -0.907281 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.046518 obj = -2.120531, rho = -0.907489 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 136 nu = 0.036434 obj = -2.335559, rho = -0.883106 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 86 nu = 0.029851 obj = -2.423865, rho = -0.647068 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 86 nu = 0.020752 obj = -2.423865, rho = -0.647068 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 86 nu = 0.014427 obj = -2.423865, rho = -0.647068 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 86 nu = 0.010029 obj = -2.423865, rho = -0.647068 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 86 nu = 0.006972 obj = -2.423865, rho = -0.647068 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 86 nu = 0.004847 obj = -2.423865, rho = -0.647068 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification)
In [ ]:
import numpy as np
import numpy.matlib as matlib
from libsvm.svmutil import *
import matplotlib.pyplot as plt
def data(N,sigma):
w = np.ones(10)/np.sqrt(10)
w1 = [1., 1., 1., 1., 1., -1., -1., -1., -1., -1.]/np.sqrt(10)
w2 = [-1., -1., 0, 1., 1., -1., -1., 0, -1., -1.]/np.sqrt(8)
x = np.zeros((4,10))
x[1,:] = x[0,:] + sigma*w1
x[2,:] = x[0,:] + sigma*w2
x[3,:] = x[2,:] + sigma*w1
X1 = x + sigma*matlib.repmat(w,4,1)/2
X2 = x - sigma*matlib.repmat(w,4,1)/2
X1 = matlib.repmat(X1,2*N,1)
X2 = matlib.repmat(X2,2*N,1)
X = np.concatenate((X1, X2), axis=0)
Y = np.concatenate((np.ones(4*2*N), -np.ones(4*2*N)),axis=0)
Z = np.random.permutation(16*N)
Z = Z[:N]
X = X[Z,:]
X = X + 0.2*sigma*np.random.randn(N,10)
Y = Y[Z]
return X, Y
# Task 2a: Generating Parameter Values
lambda_values = np.logspace(-1, 1, 20) # Logarithmically spaced values between 0.01 and 10
# Initialize arrays to store errors
training_errors = []
test_errors = []
sigma = 0.5
# Task 2b-d: Training, Testing, and Repeating the Experiment
# num_iterations = 100
for i in range(num_iterations):
# Generate data
X_train, y_train = data(100,sigma)
X_test, y_test = data(1000, sigma)
for lam in lambda_values:
# Train SVM
svm_problem_setup = svm_problem(y_train.tolist(), X_train.tolist())
param = svm_parameter(f'-t 0 -c {lam}')
model = svm_train(svm_problem_setup, param)
# Predict on training and test data
i, train_accuracy, i = svm_predict(y_train.tolist(), X_train.tolist(), model)
i, test_accuracy, i = svm_predict(y_test.tolist(), X_test.tolist(), model)
# Calculate errors
training_errors.append(100 - train_accuracy[0]) # Convert to error percentage
test_errors.append(100 - test_accuracy[0]) # Convert to error percentage
# Task 2e: Averaging Errors and Plotting
training_errors = np.array(training_errors).reshape(num_iterations, -1)
test_errors = np.array(test_errors).reshape(num_iterations, -1)
avg_training_error = np.mean(training_errors, axis=0)
avg_test_error = np.mean(test_errors, axis=0)
lambda_values_log = np.log10(lambda_values)
# Plotting
plt.figure(figsize=(10, 6))
plt.plot(lambda_values_log, avg_training_error, label='R_empirical (Average Training Error)')
plt.plot(lambda_values_log, avg_test_error, label='R_actual (Average Test Error)')
plt.plot(lambda_values_log, avg_test_error - avg_training_error, label='R_structural (Difference)')
plt.xlabel('log(λ)')
plt.ylabel('Error (%)')
plt.title('Risks vs. λ (0.1,10)@ σ = 0.5')
plt.legend()
plt.show()
* optimization finished, #iter = 47 nu = 0.920653 obj = -6.893917, rho = -0.208724 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 96% (96/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -8.071775, rho = -0.196091 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 96% (96/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 41 nu = 0.807649 obj = -9.382671, rho = -0.181255 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 39 nu = 0.747182 obj = -10.815554, rho = -0.208027 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 96% (96/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 48 nu = 0.673562 obj = -12.404873, rho = -0.127842 nSV = 70, nBSV = 65 Total nSV = 70 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 38 nu = 0.608527 obj = -14.238098, rho = -0.142273 nSV = 62, nBSV = 59 Total nSV = 62 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 53 nu = 0.548629 obj = -16.340639, rho = -0.132620 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 50 nu = 0.493627 obj = -18.724916, rho = -0.144774 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 39 nu = 0.447154 obj = -21.469439, rho = -0.134863 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 38 nu = 0.400000 obj = -24.547398, rho = -0.171606 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 35 nu = 0.356683 obj = -28.104154, rho = -0.142633 nSV = 37, nBSV = 33 Total nSV = 37 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 58 nu = 0.325209 obj = -32.102540, rho = -0.099993 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 130 nu = 0.287196 obj = -36.676279, rho = -0.083277 nSV = 34, nBSV = 24 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 95 nu = 0.260024 obj = -41.911937, rho = 0.013156 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 162 nu = 0.228853 obj = -48.054697, rho = 0.045051 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 62 nu = 0.208441 obj = -55.436593, rho = -0.083898 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 87 nu = 0.187521 obj = -63.704364, rho = -0.208576 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 60 nu = 0.168414 obj = -73.310801, rho = -0.234706 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..* optimization finished, #iter = 283 nu = 0.150719 obj = -84.589459, rho = -0.284665 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.133560 obj = -98.286541, rho = -0.310343 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -7.066350, rho = 0.102575 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 91% (91/100) (classification) Accuracy = 87.7% (877/1000) (classification) * optimization finished, #iter = 47 nu = 0.868137 obj = -8.363676, rho = 0.015350 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 94% (94/100) (classification) Accuracy = 92.7% (927/1000) (classification) * optimization finished, #iter = 46 nu = 0.822684 obj = -9.807341, rho = -0.033492 nSV = 85, nBSV = 81 Total nSV = 85 Accuracy = 95% (95/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 43 nu = 0.760000 obj = -11.420537, rho = -0.073858 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 96% (96/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 38 nu = 0.713076 obj = -13.221266, rho = -0.135900 nSV = 72, nBSV = 70 Total nSV = 72 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 40 nu = 0.648728 obj = -15.190445, rho = -0.143158 nSV = 67, nBSV = 63 Total nSV = 67 Accuracy = 96% (96/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 33 nu = 0.585490 obj = -17.395809, rho = -0.122583 nSV = 60, nBSV = 57 Total nSV = 60 Accuracy = 96% (96/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 43 nu = 0.523559 obj = -19.895625, rho = -0.099341 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 96% (96/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 45 nu = 0.475911 obj = -22.795985, rho = -0.048232 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 96% (96/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 52 nu = 0.419254 obj = -26.156579, rho = -0.064359 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.377304 obj = -30.148359, rho = -0.048596 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 67 nu = 0.335623 obj = -34.860943, rho = -0.013997 nSV = 38, nBSV = 30 Total nSV = 38 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 46 nu = 0.308547 obj = -40.546699, rho = 0.037652 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 154 nu = 0.280686 obj = -46.854192, rho = 0.118420 nSV = 32, nBSV = 23 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 49 nu = 0.252479 obj = -54.360316, rho = 0.128523 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 72 nu = 0.232677 obj = -62.921242, rho = 0.096106 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.212995 obj = -72.587846, rho = 0.116513 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *...* optimization finished, #iter = 317 nu = 0.195776 obj = -83.098242, rho = 0.261299 nSV = 25, nBSV = 15 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.171555 obj = -95.370595, rho = 0.289806 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.152905 obj = -110.204880, rho = 0.311009 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 45 nu = 0.889517 obj = -6.513745, rho = -0.402217 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 96% (96/100) (classification) Accuracy = 94% (940/1000) (classification) * optimization finished, #iter = 43 nu = 0.848427 obj = -7.545749, rho = -0.397463 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 98% (98/100) (classification) Accuracy = 95.1% (951/1000) (classification) * optimization finished, #iter = 41 nu = 0.780000 obj = -8.631912, rho = -0.407866 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 99% (99/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 45 nu = 0.709475 obj = -9.791737, rho = -0.398456 nSV = 73, nBSV = 69 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 49 nu = 0.632508 obj = -11.024532, rho = -0.373340 nSV = 67, nBSV = 59 Total nSV = 67 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 38 nu = 0.558520 obj = -12.414179, rho = -0.371263 nSV = 58, nBSV = 54 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 47 nu = 0.499911 obj = -13.916362, rho = -0.372814 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 69 nu = 0.431679 obj = -15.606091, rho = -0.370773 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 44 nu = 0.384723 obj = -17.564964, rho = -0.347898 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 80 nu = 0.340178 obj = -19.605314, rho = -0.305319 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 72 nu = 0.298086 obj = -21.927016, rho = -0.307311 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 37 nu = 0.265013 obj = -24.518114, rho = -0.303501 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 68 nu = 0.231489 obj = -27.261610, rho = -0.340398 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 67 nu = 0.202509 obj = -30.370943, rho = -0.329519 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 67 nu = 0.173149 obj = -33.962868, rho = -0.343735 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 78 nu = 0.150926 obj = -38.310406, rho = -0.425641 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.133980 obj = -43.260203, rho = -0.461205 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 51 nu = 0.121562 obj = -48.749443, rho = -0.586887 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 90 nu = 0.110591 obj = -53.920297, rho = -0.781707 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.096351 obj = -59.173110, rho = -0.838287 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -6.821038, rho = 0.050454 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 94% (94/100) (classification) Accuracy = 90.1% (901/1000) (classification) * optimization finished, #iter = 48 nu = 0.878903 obj = -7.942320, rho = -0.082082 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 51 nu = 0.809991 obj = -9.128631, rho = -0.076787 nSV = 83, nBSV = 79 Total nSV = 83 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 57 nu = 0.748711 obj = -10.390967, rho = -0.133950 nSV = 77, nBSV = 72 Total nSV = 77 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 45 nu = 0.664525 obj = -11.753432, rho = -0.134739 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 52 nu = 0.595670 obj = -13.260754, rho = -0.160467 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.537561 obj = -14.856341, rho = -0.176270 nSV = 56, nBSV = 50 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 61 nu = 0.466329 obj = -16.611224, rho = -0.170209 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 41 nu = 0.412492 obj = -18.622856, rho = -0.190284 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 25 nu = 0.363999 obj = -20.856477, rho = -0.197021 nSV = 38, nBSV = 34 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 39 nu = 0.317889 obj = -23.257496, rho = -0.253301 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 37 nu = 0.281390 obj = -25.926561, rho = -0.309784 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 59 nu = 0.247480 obj = -28.769433, rho = -0.294715 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 60 nu = 0.212318 obj = -31.871890, rho = -0.282528 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 61 nu = 0.189151 obj = -35.400873, rho = -0.318843 nSV = 22, nBSV = 17 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.164850 obj = -38.905321, rho = -0.301042 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 137 nu = 0.142131 obj = -42.761150, rho = -0.244222 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 48 nu = 0.122043 obj = -47.180797, rho = -0.201624 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 61 nu = 0.112837 obj = -51.204829, rho = -0.099564 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.099118 obj = -53.911988, rho = -0.098585 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.905655 obj = -6.818175, rho = -0.366895 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 94% (94/100) (classification) Accuracy = 95.1% (951/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -7.966612, rho = -0.292651 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 41 nu = 0.816006 obj = -9.192645, rho = -0.207712 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 58 nu = 0.748152 obj = -10.471687, rho = -0.196532 nSV = 77, nBSV = 72 Total nSV = 77 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 58 nu = 0.675033 obj = -11.852611, rho = -0.245006 nSV = 70, nBSV = 65 Total nSV = 70 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 68 nu = 0.598908 obj = -13.367450, rho = -0.221157 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 40 nu = 0.533510 obj = -15.105113, rho = -0.203459 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 37 nu = 0.472737 obj = -16.977589, rho = -0.157891 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 42 nu = 0.422582 obj = -19.001747, rho = -0.158936 nSV = 45, nBSV = 37 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 59 nu = 0.364515 obj = -21.269134, rho = -0.124857 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 56 nu = 0.315794 obj = -23.988896, rho = -0.126065 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 51 nu = 0.282149 obj = -27.124629, rho = -0.097330 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 46 nu = 0.267982 obj = -30.292300, rho = -0.030751 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.232696 obj = -33.014584, rho = 0.042828 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.196660 obj = -36.063206, rho = 0.066928 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.170260 obj = -39.496531, rho = 0.027763 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.145246 obj = -43.154943, rho = 0.016199 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 73 nu = 0.123941 obj = -47.283634, rho = 0.067498 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 67 nu = 0.108174 obj = -51.766607, rho = 0.092589 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 66 nu = 0.092132 obj = -56.491967, rho = 0.119752 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -6.850651, rho = -0.537696 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 86% (86/100) (classification) Accuracy = 82.2% (822/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -8.118212, rho = -0.410897 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 95% (95/100) (classification) Accuracy = 94.3% (943/1000) (classification) * optimization finished, #iter = 46 nu = 0.820000 obj = -9.435902, rho = -0.374152 nSV = 82, nBSV = 81 Total nSV = 82 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 42 nu = 0.756636 obj = -10.855947, rho = -0.321991 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 55 nu = 0.689733 obj = -12.387917, rho = -0.299866 nSV = 71, nBSV = 65 Total nSV = 71 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 41 nu = 0.620000 obj = -14.108316, rho = -0.259028 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 50 nu = 0.556406 obj = -16.005896, rho = -0.204557 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 38 nu = 0.496418 obj = -18.117264, rho = -0.222341 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 84 nu = 0.445469 obj = -20.443099, rho = -0.262809 nSV = 49, nBSV = 40 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 48 nu = 0.400000 obj = -22.971875, rho = -0.281301 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 28 nu = 0.354535 obj = -25.594983, rho = -0.266614 nSV = 36, nBSV = 33 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 73 nu = 0.319112 obj = -28.267312, rho = -0.259807 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.275898 obj = -30.965221, rho = -0.248898 nSV = 32, nBSV = 23 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 64 nu = 0.235814 obj = -33.890551, rho = -0.267845 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 35 nu = 0.201566 obj = -37.198785, rho = -0.256908 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 81 nu = 0.179695 obj = -40.431141, rho = -0.314611 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.154383 obj = -43.420742, rho = -0.417940 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *....* optimization finished, #iter = 407 nu = 0.129255 obj = -46.586064, rho = -0.430341 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) ..* optimization finished, #iter = 255 nu = 0.107712 obj = -50.143141, rho = -0.495412 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 166 nu = 0.091073 obj = -54.110014, rho = -0.587226 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -6.613752, rho = 0.088904 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 95% (95/100) (classification) Accuracy = 91.1% (911/1000) (classification) * optimization finished, #iter = 50 nu = 0.841987 obj = -7.689214, rho = -0.044152 nSV = 88, nBSV = 83 Total nSV = 88 Accuracy = 97% (97/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 49 nu = 0.784132 obj = -8.862002, rho = -0.017108 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 56 nu = 0.709798 obj = -10.130637, rho = -0.051673 nSV = 74, nBSV = 69 Total nSV = 74 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 56 nu = 0.633119 obj = -11.580416, rho = -0.071414 nSV = 68, nBSV = 61 Total nSV = 68 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 34 nu = 0.576196 obj = -13.239456, rho = -0.008454 nSV = 59, nBSV = 56 Total nSV = 59 Accuracy = 96% (96/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 40 nu = 0.517437 obj = -15.054211, rho = -0.014202 nSV = 53, nBSV = 49 Total nSV = 53 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 45 nu = 0.462098 obj = -17.107722, rho = -0.024147 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 96% (96/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 51 nu = 0.412705 obj = -19.378798, rho = -0.010805 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 48 nu = 0.361765 obj = -22.074501, rho = 0.000441 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 47 nu = 0.323077 obj = -25.277325, rho = -0.068191 nSV = 35, nBSV = 31 Total nSV = 35 Accuracy = 96% (96/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 24 nu = 0.289126 obj = -28.959974, rho = -0.022057 nSV = 31, nBSV = 28 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 81 nu = 0.260680 obj = -33.118036, rho = -0.033055 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 84 nu = 0.226949 obj = -38.148970, rho = -0.040232 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 35 nu = 0.201953 obj = -44.420419, rho = -0.075504 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 96% (96/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 73 nu = 0.183440 obj = -52.100467, rho = -0.116312 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 96% (96/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 76 nu = 0.167463 obj = -61.322034, rho = -0.135273 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 95% (95/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 88 nu = 0.152330 obj = -72.556953, rho = -0.133783 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 48 nu = 0.142384 obj = -86.077658, rho = -0.137609 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 58 nu = 0.136117 obj = -101.575793, rho = -0.151506 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -6.800184, rho = -0.322408 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 98% (98/100) (classification) Accuracy = 94.8% (948/1000) (classification) * optimization finished, #iter = 46 nu = 0.865303 obj = -7.947035, rho = -0.252260 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 44 nu = 0.801783 obj = -9.192233, rho = -0.190533 nSV = 82, nBSV = 79 Total nSV = 82 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 46 nu = 0.733301 obj = -10.563201, rho = -0.160039 nSV = 75, nBSV = 71 Total nSV = 75 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 42 nu = 0.663252 obj = -12.088045, rho = -0.171110 nSV = 69, nBSV = 64 Total nSV = 69 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 41 nu = 0.594089 obj = -13.820207, rho = -0.179478 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 32 nu = 0.540228 obj = -15.799684, rho = -0.243183 nSV = 56, nBSV = 53 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.499350 obj = -17.864525, rho = -0.201742 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.447872 obj = -20.012444, rho = -0.281943 nSV = 47, nBSV = 40 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 38 nu = 0.387077 obj = -22.354324, rho = -0.274253 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 83 nu = 0.347216 obj = -24.875617, rho = -0.236266 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 64 nu = 0.308131 obj = -27.493605, rho = -0.251688 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 89 nu = 0.262415 obj = -30.256062, rho = -0.288259 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 64 nu = 0.231809 obj = -33.354168, rho = -0.276286 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 73 nu = 0.201466 obj = -36.308609, rho = -0.236027 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *..* optimization finished, #iter = 200 nu = 0.169565 obj = -39.672076, rho = -0.249507 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 69 nu = 0.143731 obj = -43.511615, rho = -0.253793 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 81 nu = 0.127831 obj = -47.555142, rho = -0.185251 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 92 nu = 0.111016 obj = -51.689306, rho = -0.205947 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 89 nu = 0.098002 obj = -54.908945, rho = -0.231898 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -7.169704, rho = -0.530187 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 90% (90/100) (classification) Accuracy = 87.5% (875/1000) (classification) * optimization finished, #iter = 48 nu = 0.883777 obj = -8.457447, rho = -0.456166 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 95% (95/100) (classification) Accuracy = 92.9% (929/1000) (classification) * optimization finished, #iter = 45 nu = 0.835033 obj = -9.875820, rho = -0.401109 nSV = 86, nBSV = 81 Total nSV = 86 Accuracy = 95% (95/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 39 nu = 0.780000 obj = -11.437388, rho = -0.330369 nSV = 78, nBSV = 78 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 42 nu = 0.720000 obj = -13.162606, rho = -0.318358 nSV = 73, nBSV = 70 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 69 nu = 0.655594 obj = -14.979886, rho = -0.254566 nSV = 69, nBSV = 64 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 35 nu = 0.586977 obj = -17.038102, rho = -0.264381 nSV = 60, nBSV = 57 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 35 nu = 0.524347 obj = -19.310782, rho = -0.321722 nSV = 57, nBSV = 51 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 37 nu = 0.477774 obj = -21.734238, rho = -0.281662 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 49 nu = 0.423488 obj = -24.382134, rho = -0.239877 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 68 nu = 0.374916 obj = -27.169728, rho = -0.288483 nSV = 41, nBSV = 33 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 61 nu = 0.325868 obj = -30.310499, rho = -0.265375 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 75 nu = 0.287421 obj = -33.864692, rho = -0.237342 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 71 nu = 0.255916 obj = -37.631601, rho = -0.252005 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 157 nu = 0.222326 obj = -41.472512, rho = -0.186471 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 92 nu = 0.192841 obj = -45.817263, rho = -0.168513 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.173041 obj = -50.105262, rho = -0.126725 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.150107 obj = -53.995943, rho = -0.091213 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.125246 obj = -57.896423, rho = -0.061456 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 268 nu = 0.107271 obj = -62.101157, rho = -0.051312 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 48 nu = 0.956129 obj = -7.361636, rho = -0.009889 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 96% (96/100) (classification) Accuracy = 94.7% (947/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -8.674427, rho = -0.060955 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 98% (98/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 44 nu = 0.858942 obj = -10.131080, rho = -0.106749 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 41 nu = 0.800000 obj = -11.736026, rho = -0.103008 nSV = 80, nBSV = 80 Total nSV = 80 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 48 nu = 0.727295 obj = -13.488804, rho = -0.152975 nSV = 74, nBSV = 71 Total nSV = 74 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 46 nu = 0.671183 obj = -15.395603, rho = -0.124631 nSV = 70, nBSV = 65 Total nSV = 70 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 41 nu = 0.601083 obj = -17.544395, rho = -0.143711 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 42 nu = 0.539801 obj = -19.941992, rho = -0.121169 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 36 nu = 0.482445 obj = -22.613222, rho = -0.104654 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 36 nu = 0.427673 obj = -25.624454, rho = -0.077446 nSV = 45, nBSV = 42 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 66 nu = 0.381384 obj = -28.969601, rho = -0.090067 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 45 nu = 0.332897 obj = -32.895676, rho = -0.097142 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 27 nu = 0.300000 obj = -37.440487, rho = -0.113820 nSV = 31, nBSV = 27 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 43 nu = 0.270392 obj = -42.300942, rho = -0.150723 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 70 nu = 0.244667 obj = -47.730215, rho = -0.139853 nSV = 27, nBSV = 22 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) *...* optimization finished, #iter = 312 nu = 0.218067 obj = -53.235155, rho = -0.199710 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.187246 obj = -59.582045, rho = -0.233289 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 123 nu = 0.164932 obj = -66.976735, rho = -0.242581 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 183 nu = 0.145030 obj = -75.417888, rho = -0.282983 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.125988 obj = -85.356676, rho = -0.288977 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -6.706609, rho = -0.141975 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 96% (96/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 47 nu = 0.841844 obj = -7.829029, rho = -0.181233 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 40 nu = 0.785675 obj = -9.057406, rho = -0.295067 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.728985 obj = -10.379018, rho = -0.259648 nSV = 76, nBSV = 71 Total nSV = 76 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 55 nu = 0.660789 obj = -11.837113, rho = -0.218675 nSV = 69, nBSV = 63 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.591113 obj = -13.471700, rho = -0.209194 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 49 nu = 0.526297 obj = -15.319887, rho = -0.207530 nSV = 56, nBSV = 49 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 71 nu = 0.468612 obj = -17.428117, rho = -0.214012 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 61 nu = 0.423325 obj = -19.800150, rho = -0.192258 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 53 nu = 0.385839 obj = -22.392236, rho = -0.124968 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 76 nu = 0.337943 obj = -25.148123, rho = -0.091006 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 53 nu = 0.300664 obj = -28.227763, rho = -0.111175 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 172 nu = 0.264180 obj = -31.565322, rho = -0.118061 nSV = 31, nBSV = 21 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 70 nu = 0.228445 obj = -35.479384, rho = -0.110696 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 72 nu = 0.204259 obj = -40.012306, rho = -0.080788 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 86 nu = 0.181711 obj = -44.801812, rho = -0.076601 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 56 nu = 0.160291 obj = -50.014660, rho = -0.061987 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 48 nu = 0.146962 obj = -55.154542, rho = 0.072471 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 235 nu = 0.131050 obj = -59.511519, rho = 0.205405 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.112363 obj = -63.292564, rho = 0.239255 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -6.267033, rho = -0.348940 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 94% (94/100) (classification) Accuracy = 93.5% (935/1000) (classification) * optimization finished, #iter = 40 nu = 0.795578 obj = -7.303017, rho = -0.279997 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 96% (96/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 41 nu = 0.729484 obj = -8.462397, rho = -0.306798 nSV = 74, nBSV = 71 Total nSV = 74 Accuracy = 96% (96/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 42 nu = 0.680000 obj = -9.717842, rho = -0.257419 nSV = 69, nBSV = 67 Total nSV = 69 Accuracy = 96% (96/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 35 nu = 0.614519 obj = -11.093163, rho = -0.219663 nSV = 63, nBSV = 60 Total nSV = 63 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 35 nu = 0.560000 obj = -12.586559, rho = -0.232503 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 36 nu = 0.495803 obj = -14.239024, rho = -0.239624 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 28 nu = 0.432719 obj = -16.168995, rho = -0.238217 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 36 nu = 0.392766 obj = -18.362319, rho = -0.214273 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 34 nu = 0.350069 obj = -20.760470, rho = -0.253950 nSV = 37, nBSV = 34 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 62 nu = 0.311001 obj = -23.401532, rho = -0.236268 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.271330 obj = -26.523609, rho = -0.230478 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 92 nu = 0.237636 obj = -30.270453, rho = -0.220192 nSV = 29, nBSV = 19 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 50 nu = 0.213408 obj = -34.820258, rho = -0.249273 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 53 nu = 0.197504 obj = -39.694490, rho = -0.298657 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 34 nu = 0.185509 obj = -44.627183, rho = -0.176068 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 73 nu = 0.166860 obj = -48.669121, rho = -0.040352 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.142876 obj = -52.849731, rho = -0.044385 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) .*..* optimization finished, #iter = 329 nu = 0.119441 obj = -57.591972, rho = -0.009754 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 160 nu = 0.101070 obj = -63.135417, rho = -0.028033 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 52 nu = 0.934505 obj = -6.901413, rho = -0.272236 nSV = 94, nBSV = 91 Total nSV = 94 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 48 nu = 0.855531 obj = -8.072136, rho = -0.252124 nSV = 88, nBSV = 84 Total nSV = 88 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 43 nu = 0.804351 obj = -9.411501, rho = -0.222025 nSV = 82, nBSV = 79 Total nSV = 82 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 39 nu = 0.746371 obj = -10.847244, rho = -0.161704 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 35 nu = 0.690215 obj = -12.394337, rho = -0.124911 nSV = 70, nBSV = 68 Total nSV = 70 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 51 nu = 0.619933 obj = -14.040849, rho = -0.119125 nSV = 65, nBSV = 59 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 46 nu = 0.557150 obj = -15.882343, rho = -0.106328 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 73 nu = 0.496081 obj = -17.861083, rho = -0.013058 nSV = 53, nBSV = 46 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 39 nu = 0.438756 obj = -20.064735, rho = -0.030895 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 47 nu = 0.397736 obj = -22.357728, rho = 0.003604 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 56 nu = 0.344617 obj = -24.800505, rho = -0.021149 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 59 nu = 0.298724 obj = -27.521155, rho = -0.058731 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 63 nu = 0.259752 obj = -30.553057, rho = -0.055799 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 74 nu = 0.225339 obj = -34.047229, rho = -0.044329 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 64 nu = 0.197709 obj = -37.980733, rho = -0.055133 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 70 nu = 0.171695 obj = -42.454012, rho = -0.062179 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 67 nu = 0.152167 obj = -47.471327, rho = -0.015757 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 77 nu = 0.130737 obj = -53.105819, rho = 0.005149 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 69 nu = 0.116456 obj = -59.620266, rho = 0.143063 nSV = 14, nBSV = 9 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 72 nu = 0.104669 obj = -66.144509, rho = 0.177936 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.903727 obj = -6.706177, rho = -0.231181 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 97% (97/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 46 nu = 0.855560 obj = -7.815368, rho = -0.231158 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 98% (98/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 42 nu = 0.781994 obj = -9.025535, rho = -0.154632 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 97% (97/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 47 nu = 0.722284 obj = -10.355825, rho = -0.134012 nSV = 74, nBSV = 71 Total nSV = 74 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 46 nu = 0.652032 obj = -11.808425, rho = -0.121004 nSV = 67, nBSV = 62 Total nSV = 67 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 59 nu = 0.593890 obj = -13.404818, rho = -0.080977 nSV = 64, nBSV = 58 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 48 nu = 0.541367 obj = -15.090674, rho = -0.120854 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 66 nu = 0.473491 obj = -16.861202, rho = -0.128525 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 44 nu = 0.415301 obj = -18.871264, rho = -0.115890 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 51 nu = 0.366267 obj = -21.143271, rho = -0.074451 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 69 nu = 0.318945 obj = -23.714606, rho = -0.049406 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 41 nu = 0.282396 obj = -26.694462, rho = -0.028675 nSV = 31, nBSV = 27 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 59 nu = 0.251746 obj = -29.749273, rho = -0.020768 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.217357 obj = -33.338653, rho = -0.021211 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .**.* optimization finished, #iter = 151 nu = 0.193407 obj = -37.335851, rho = 0.013794 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 68 nu = 0.168442 obj = -41.862872, rho = -0.009963 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.148052 obj = -46.962426, rho = -0.058349 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *..* optimization finished, #iter = 253 nu = 0.132079 obj = -52.361706, rho = -0.100423 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 81 nu = 0.117425 obj = -58.332821, rho = -0.122363 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 88 nu = 0.102847 obj = -64.200554, rho = -0.186695 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -6.372040, rho = -0.574342 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 88% (88/100) (classification) Accuracy = 83.6% (836/1000) (classification) * optimization finished, #iter = 44 nu = 0.807795 obj = -7.439749, rho = -0.532563 nSV = 82, nBSV = 79 Total nSV = 82 Accuracy = 95% (95/100) (classification) Accuracy = 89.3% (893/1000) (classification) * optimization finished, #iter = 42 nu = 0.760000 obj = -8.586858, rho = -0.460530 nSV = 77, nBSV = 74 Total nSV = 77 Accuracy = 98% (98/100) (classification) Accuracy = 94.1% (941/1000) (classification) * optimization finished, #iter = 43 nu = 0.691287 obj = -9.793425, rho = -0.437999 nSV = 72, nBSV = 68 Total nSV = 72 Accuracy = 99% (99/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 43 nu = 0.616750 obj = -11.143498, rho = -0.423403 nSV = 63, nBSV = 60 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 42 nu = 0.560000 obj = -12.673859, rho = -0.406856 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 27 nu = 0.503319 obj = -14.311254, rho = -0.350149 nSV = 52, nBSV = 50 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.447644 obj = -16.040360, rho = -0.336746 nSV = 48, nBSV = 40 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 37 nu = 0.393225 obj = -18.057190, rho = -0.321073 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 23 nu = 0.347099 obj = -20.325685, rho = -0.349622 nSV = 37, nBSV = 34 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 26 nu = 0.316527 obj = -22.679405, rho = -0.354146 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 29 nu = 0.278895 obj = -25.155013, rho = -0.352974 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 55 nu = 0.240544 obj = -27.768186, rho = -0.333357 nSV = 27, nBSV = 22 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 46 nu = 0.210775 obj = -30.654904, rho = -0.492211 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 55 nu = 0.183967 obj = -33.618903, rho = -0.606732 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 42 nu = 0.165218 obj = -36.621969, rho = -0.657469 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 57 nu = 0.141372 obj = -39.120953, rho = -0.681751 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.119305 obj = -41.399057, rho = -0.693992 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 96 nu = 0.102116 obj = -43.344478, rho = -0.600057 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .**...........* optimization finished, #iter = 1220 nu = 0.084281 obj = -44.805699, rho = -0.584526 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -6.845388, rho = 0.140807 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 92% (92/100) (classification) Accuracy = 87.1% (871/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -7.994982, rho = 0.008031 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 48 nu = 0.812171 obj = -9.203529, rho = -0.003833 nSV = 83, nBSV = 79 Total nSV = 83 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 50 nu = 0.748121 obj = -10.519965, rho = -0.025066 nSV = 77, nBSV = 74 Total nSV = 77 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 46 nu = 0.666109 obj = -11.937279, rho = -0.041817 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.601130 obj = -13.518021, rho = -0.116662 nSV = 63, nBSV = 56 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 32 nu = 0.536490 obj = -15.289767, rho = -0.100465 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 39 nu = 0.473473 obj = -17.250034, rho = -0.055348 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 48 nu = 0.419170 obj = -19.476742, rho = -0.086583 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 34 nu = 0.374935 obj = -21.940373, rho = -0.125277 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 39 nu = 0.330643 obj = -24.701885, rho = -0.081167 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 40 nu = 0.294560 obj = -27.770100, rho = -0.098485 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 60 nu = 0.261106 obj = -31.026960, rho = -0.067097 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 60 nu = 0.229051 obj = -34.692845, rho = -0.036974 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 68 nu = 0.205584 obj = -38.715327, rho = -0.067280 nSV = 23, nBSV = 19 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.180111 obj = -42.643474, rho = -0.086153 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 66 nu = 0.161344 obj = -46.783817, rho = -0.041319 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 72 nu = 0.142250 obj = -50.258734, rho = -0.081823 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) .* optimization finished, #iter = 163 nu = 0.119018 obj = -53.375832, rho = -0.108784 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) .*.* optimization finished, #iter = 225 nu = 0.100840 obj = -56.507358, rho = -0.077231 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -6.693814, rho = 0.062619 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 98% (98/100) (classification) Accuracy = 92.1% (921/1000) (classification) * optimization finished, #iter = 50 nu = 0.857748 obj = -7.780495, rho = -0.058028 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 99% (99/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 43 nu = 0.793083 obj = -8.953549, rho = -0.063910 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 49 nu = 0.718829 obj = -10.220923, rho = -0.073595 nSV = 75, nBSV = 70 Total nSV = 75 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 37 nu = 0.657498 obj = -11.659714, rho = -0.051792 nSV = 67, nBSV = 64 Total nSV = 67 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 42 nu = 0.586437 obj = -13.215381, rho = -0.083556 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 51 nu = 0.523668 obj = -14.923341, rho = -0.053208 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 42 nu = 0.467724 obj = -16.769157, rho = -0.127234 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 40 nu = 0.411745 obj = -18.853807, rho = -0.176533 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 61 nu = 0.365312 obj = -21.169610, rho = -0.117304 nSV = 38, nBSV = 33 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 37 nu = 0.325374 obj = -23.720701, rho = -0.039515 nSV = 35, nBSV = 30 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 58 nu = 0.284330 obj = -26.423298, rho = -0.020361 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 50 nu = 0.244324 obj = -29.566397, rho = -0.060819 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 40 nu = 0.215690 obj = -33.244343, rho = -0.024295 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 61 nu = 0.196622 obj = -37.068348, rho = 0.035506 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 51 nu = 0.169583 obj = -41.195860, rho = 0.080183 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 38 nu = 0.148154 obj = -45.902422, rho = 0.096889 nSV = 17, nBSV = 12 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 60 nu = 0.130531 obj = -50.733332, rho = 0.061408 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 87 nu = 0.110902 obj = -56.393660, rho = 0.036160 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 80 nu = 0.095694 obj = -63.221736, rho = 0.049930 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 49 nu = 0.918339 obj = -6.881465, rho = -0.289892 nSV = 93, nBSV = 90 Total nSV = 93 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 49 nu = 0.862882 obj = -8.056760, rho = -0.306107 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 46 nu = 0.804350 obj = -9.371627, rho = -0.265760 nSV = 83, nBSV = 79 Total nSV = 83 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 44 nu = 0.736034 obj = -10.845000, rho = -0.248807 nSV = 76, nBSV = 72 Total nSV = 76 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 38 nu = 0.666590 obj = -12.510968, rho = -0.228635 nSV = 68, nBSV = 65 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 49 nu = 0.625120 obj = -14.377422, rho = -0.115528 nSV = 65, nBSV = 60 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 45 nu = 0.560186 obj = -16.389907, rho = -0.130715 nSV = 60, nBSV = 53 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 62 nu = 0.502474 obj = -18.628693, rho = -0.111406 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 50 nu = 0.455219 obj = -21.131113, rho = -0.091225 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 68 nu = 0.398770 obj = -23.889896, rho = -0.027578 nSV = 45, nBSV = 36 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 38 nu = 0.355396 obj = -27.106373, rho = -0.073039 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 34 nu = 0.326256 obj = -30.541151, rho = 0.002530 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 37 nu = 0.291263 obj = -34.035896, rho = -0.044315 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.250726 obj = -37.838676, rho = -0.035072 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 42 nu = 0.214547 obj = -42.489956, rho = -0.031071 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 80 nu = 0.192278 obj = -47.870463, rho = -0.087811 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.168261 obj = -53.897851, rho = -0.074860 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 67 nu = 0.148827 obj = -60.692394, rho = -0.087576 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.130474 obj = -68.175579, rho = -0.142630 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 94 nu = 0.114500 obj = -77.252346, rho = -0.196946 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 44 nu = 0.872502 obj = -6.712718, rho = -0.007363 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 92% (92/100) (classification) Accuracy = 93.2% (932/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -7.931893, rho = -0.090101 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 95% (95/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 40 nu = 0.785548 obj = -9.249763, rho = -0.050992 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 96% (96/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 45 nu = 0.732771 obj = -10.713856, rho = -0.014262 nSV = 74, nBSV = 69 Total nSV = 74 Accuracy = 95% (95/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 48 nu = 0.680190 obj = -12.289230, rho = -0.073314 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 40 nu = 0.603594 obj = -14.017817, rho = -0.106603 nSV = 63, nBSV = 60 Total nSV = 63 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 38 nu = 0.546862 obj = -15.980842, rho = -0.145965 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 42 nu = 0.487073 obj = -18.192799, rho = -0.161197 nSV = 52, nBSV = 45 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.434834 obj = -20.742898, rho = -0.218802 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 34 nu = 0.382365 obj = -23.751092, rho = -0.215575 nSV = 40, nBSV = 37 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 34 nu = 0.358181 obj = -27.105248, rho = -0.242337 nSV = 37, nBSV = 33 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 65 nu = 0.313093 obj = -30.722189, rho = -0.276310 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 72 nu = 0.278859 obj = -34.959424, rho = -0.264905 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 75 nu = 0.251514 obj = -39.722249, rho = -0.175232 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 89 nu = 0.222069 obj = -45.172917, rho = -0.224015 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 60 nu = 0.197391 obj = -51.653013, rho = -0.289386 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 56 nu = 0.178801 obj = -58.776297, rho = -0.352893 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 95 nu = 0.161596 obj = -66.756813, rho = -0.417758 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 192 nu = 0.140082 obj = -75.783490, rho = -0.411607 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 99 nu = 0.123481 obj = -86.980115, rho = -0.385193 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.942067 obj = -7.345989, rho = -0.268589 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 94% (94/100) (classification) Accuracy = 92.6% (926/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -8.654887, rho = -0.176437 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 48 nu = 0.859694 obj = -10.051220, rho = -0.167753 nSV = 89, nBSV = 84 Total nSV = 89 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 42 nu = 0.789987 obj = -11.644881, rho = -0.119770 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.729239 obj = -13.364361, rho = -0.094112 nSV = 75, nBSV = 70 Total nSV = 75 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 37 nu = 0.656999 obj = -15.338451, rho = -0.145634 nSV = 67, nBSV = 64 Total nSV = 67 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 44 nu = 0.603467 obj = -17.493664, rho = -0.105052 nSV = 63, nBSV = 57 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 49 nu = 0.539987 obj = -19.864353, rho = -0.146245 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 64 nu = 0.480732 obj = -22.520372, rho = -0.137207 nSV = 52, nBSV = 43 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 77 nu = 0.422058 obj = -25.583619, rho = -0.122666 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 69 nu = 0.371935 obj = -29.170052, rho = -0.154173 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 42 nu = 0.335070 obj = -33.402110, rho = -0.222930 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 43 nu = 0.308464 obj = -38.013302, rho = -0.169003 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 70 nu = 0.272152 obj = -43.110127, rho = -0.147417 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 56 nu = 0.239412 obj = -49.082669, rho = -0.128202 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 77 nu = 0.213247 obj = -56.041944, rho = -0.130964 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 67 nu = 0.187941 obj = -64.503271, rho = -0.139952 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.171797 obj = -74.343596, rho = -0.170954 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.158163 obj = -84.772534, rho = -0.282793 nSV = 23, nBSV = 12 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 123 nu = 0.139032 obj = -97.057972, rho = -0.378863 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -7.036974, rho = -0.237486 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 97% (97/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 48 nu = 0.880000 obj = -8.273207, rho = -0.208120 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 97% (97/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 47 nu = 0.817701 obj = -9.653808, rho = -0.210890 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 49 nu = 0.755288 obj = -11.194396, rho = -0.262625 nSV = 78, nBSV = 74 Total nSV = 78 Accuracy = 96% (96/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 47 nu = 0.688059 obj = -12.957745, rho = -0.275595 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 96% (96/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.629425 obj = -14.971063, rho = -0.261676 nSV = 64, nBSV = 61 Total nSV = 64 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 42 nu = 0.573252 obj = -17.177220, rho = -0.220664 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 50 nu = 0.520239 obj = -19.668581, rho = -0.185864 nSV = 54, nBSV = 51 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 42 nu = 0.468473 obj = -22.509535, rho = -0.161905 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 54 nu = 0.422550 obj = -25.702142, rho = -0.084062 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 48 nu = 0.380000 obj = -29.302774, rho = -0.085224 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 58 nu = 0.347857 obj = -33.110249, rho = -0.213741 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.306376 obj = -37.246508, rho = -0.262856 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 80 nu = 0.265925 obj = -42.105353, rho = -0.293150 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 186 nu = 0.233081 obj = -47.941173, rho = -0.253109 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 67 nu = 0.215333 obj = -54.688990, rho = -0.195668 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 73 nu = 0.189671 obj = -61.824756, rho = -0.180218 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.167161 obj = -70.263257, rho = -0.165365 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 151 nu = 0.148186 obj = -80.076094, rho = -0.114725 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 175 nu = 0.133693 obj = -91.203725, rho = -0.028743 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -7.095504, rho = 0.196322 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 93% (93/100) (classification) Accuracy = 88.6% (886/1000) (classification) * optimization finished, #iter = 48 nu = 0.904489 obj = -8.312298, rho = 0.031546 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 97% (97/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 49 nu = 0.855422 obj = -9.574524, rho = 0.003908 nSV = 86, nBSV = 82 Total nSV = 86 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 45 nu = 0.775833 obj = -10.918115, rho = -0.035340 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 62 nu = 0.698204 obj = -12.374084, rho = -0.088575 nSV = 73, nBSV = 68 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.620879 obj = -14.015187, rho = -0.117682 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 36 nu = 0.554218 obj = -15.814890, rho = -0.063750 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 36 nu = 0.497874 obj = -17.806603, rho = -0.079894 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 41 nu = 0.443683 obj = -19.873153, rho = -0.108947 nSV = 47, nBSV = 40 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 66 nu = 0.385461 obj = -22.142835, rho = -0.102278 nSV = 42, nBSV = 34 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 74 nu = 0.332744 obj = -24.825077, rho = -0.110263 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 59 nu = 0.294527 obj = -27.949145, rho = -0.062291 nSV = 34, nBSV = 25 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.256394 obj = -31.480211, rho = -0.028137 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 74 nu = 0.226691 obj = -35.692868, rho = -0.041976 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 88 nu = 0.197538 obj = -40.572577, rho = -0.109950 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.173774 obj = -46.616341, rho = -0.094281 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 89 nu = 0.153871 obj = -53.973294, rho = -0.062824 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 59 nu = 0.139882 obj = -62.793829, rho = -0.108018 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 32 nu = 0.130468 obj = -72.901916, rho = -0.209453 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.119278 obj = -84.085370, rho = -0.239102 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 51 nu = 0.891798 obj = -6.490916, rho = -0.245196 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 99% (99/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 45 nu = 0.839349 obj = -7.529185, rho = -0.252059 nSV = 84, nBSV = 82 Total nSV = 84 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 47 nu = 0.765115 obj = -8.652350, rho = -0.177381 nSV = 79, nBSV = 75 Total nSV = 79 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 41 nu = 0.700000 obj = -9.880200, rho = -0.132564 nSV = 72, nBSV = 68 Total nSV = 72 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 42 nu = 0.622842 obj = -11.243164, rho = -0.115687 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 53 nu = 0.559244 obj = -12.793394, rho = -0.125947 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 36 nu = 0.500000 obj = -14.581194, rho = -0.138424 nSV = 51, nBSV = 48 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.447269 obj = -16.553406, rho = -0.190714 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.397463 obj = -18.815547, rho = -0.259896 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.366294 obj = -21.273000, rho = -0.229385 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 74 nu = 0.327990 obj = -23.821173, rho = -0.272159 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 40 nu = 0.289488 obj = -26.543795, rho = -0.224936 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 57 nu = 0.257093 obj = -29.372698, rho = -0.230650 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 64 nu = 0.220376 obj = -32.398948, rho = -0.231820 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 76 nu = 0.189792 obj = -35.760787, rho = -0.272772 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 76 nu = 0.165935 obj = -39.652155, rho = -0.342088 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 85 nu = 0.144601 obj = -43.747266, rho = -0.417219 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 82 nu = 0.127151 obj = -47.998716, rho = -0.521445 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 93 nu = 0.106976 obj = -52.693604, rho = -0.542702 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.093614 obj = -58.109198, rho = -0.657051 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 46 nu = 0.895542 obj = -6.622383, rho = -0.322907 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 95% (95/100) (classification) Accuracy = 94.2% (942/1000) (classification) * optimization finished, #iter = 50 nu = 0.834163 obj = -7.731372, rho = -0.289153 nSV = 85, nBSV = 81 Total nSV = 85 Accuracy = 96% (96/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 41 nu = 0.780126 obj = -8.968436, rho = -0.252015 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 39 nu = 0.718934 obj = -10.288271, rho = -0.239998 nSV = 73, nBSV = 70 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 36 nu = 0.660000 obj = -11.716668, rho = -0.186336 nSV = 67, nBSV = 65 Total nSV = 67 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 37 nu = 0.584832 obj = -13.255191, rho = -0.160132 nSV = 61, nBSV = 58 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 61 nu = 0.519906 obj = -15.006228, rho = -0.141731 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 33 nu = 0.456298 obj = -17.064200, rho = -0.178007 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.423394 obj = -19.303006, rho = -0.145293 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 77 nu = 0.366067 obj = -21.754319, rho = -0.144378 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 42 nu = 0.332130 obj = -24.594804, rho = -0.121345 nSV = 35, nBSV = 30 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 86 nu = 0.291006 obj = -27.544134, rho = -0.117471 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 73 nu = 0.253297 obj = -31.063824, rho = -0.099773 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 58 nu = 0.222795 obj = -35.130854, rho = -0.102715 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 47 nu = 0.199214 obj = -39.766879, rho = -0.028986 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 35 nu = 0.178019 obj = -44.929376, rho = -0.058607 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 36 nu = 0.161096 obj = -50.509348, rho = -0.216550 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 76 nu = 0.146659 obj = -55.943664, rho = -0.329014 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.126528 obj = -61.522448, rho = -0.396282 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 70 nu = 0.110573 obj = -67.391101, rho = -0.491075 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.922565 obj = -7.209074, rho = -0.253096 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 88% (88/100) (classification) Accuracy = 93.7% (937/1000) (classification) * optimization finished, #iter = 47 nu = 0.888687 obj = -8.511366, rho = -0.165522 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 92% (92/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 50 nu = 0.832945 obj = -9.979461, rho = -0.177607 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 93% (93/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 59 nu = 0.766181 obj = -11.680855, rho = -0.146991 nSV = 79, nBSV = 74 Total nSV = 79 Accuracy = 93% (93/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 43 nu = 0.710631 obj = -13.623589, rho = -0.102154 nSV = 73, nBSV = 69 Total nSV = 73 Accuracy = 94% (94/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 48 nu = 0.658108 obj = -15.806999, rho = -0.093675 nSV = 68, nBSV = 63 Total nSV = 68 Accuracy = 93% (93/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 46 nu = 0.597651 obj = -18.271658, rho = -0.082350 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 94% (94/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 40 nu = 0.540000 obj = -21.137456, rho = -0.111784 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 96% (96/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 38 nu = 0.495294 obj = -24.458722, rho = -0.067218 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 96% (96/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 154 nu = 0.442672 obj = -28.257462, rho = -0.060530 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 74 nu = 0.397465 obj = -32.909509, rho = -0.055166 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 49 nu = 0.364185 obj = -38.441273, rho = -0.043832 nSV = 39, nBSV = 35 Total nSV = 39 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 41 nu = 0.336805 obj = -44.701964, rho = 0.011029 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 74 nu = 0.302875 obj = -52.014818, rho = 0.017903 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.274356 obj = -60.806835, rho = -0.011405 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 86 nu = 0.249882 obj = -71.410994, rho = -0.065503 nSV = 33, nBSV = 23 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 85 nu = 0.236545 obj = -83.815574, rho = 0.003773 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 92 nu = 0.215362 obj = -98.043678, rho = -0.005814 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 83 nu = 0.198107 obj = -114.569148, rho = -0.020449 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 70 nu = 0.185486 obj = -133.459575, rho = -0.070219 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -7.021877, rho = -0.316928 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 97% (97/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 45 nu = 0.886820 obj = -8.186186, rho = -0.206820 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.845951 obj = -9.422451, rho = -0.217212 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.769298 obj = -10.711724, rho = -0.213957 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 39 nu = 0.696591 obj = -12.067181, rho = -0.199195 nSV = 71, nBSV = 68 Total nSV = 71 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 54 nu = 0.605045 obj = -13.584349, rho = -0.188946 nSV = 63, nBSV = 56 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 49 nu = 0.533912 obj = -15.386547, rho = -0.183888 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 53 nu = 0.477858 obj = -17.411594, rho = -0.141576 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 73 nu = 0.426428 obj = -19.571553, rho = -0.093455 nSV = 47, nBSV = 38 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 61 nu = 0.379123 obj = -22.017831, rho = -0.170588 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 75 nu = 0.334140 obj = -24.775491, rho = -0.151775 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 58 nu = 0.293146 obj = -27.763925, rho = -0.163985 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 180 nu = 0.264731 obj = -31.069043, rho = -0.120260 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 138 nu = 0.232972 obj = -34.435963, rho = -0.078975 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *...* optimization finished, #iter = 365 nu = 0.199609 obj = -38.146822, rho = -0.087039 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 166 nu = 0.173666 obj = -42.555588, rho = -0.067269 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.159923 obj = -47.249198, rho = -0.157838 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 94 nu = 0.142065 obj = -51.267377, rho = -0.146269 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ..*.....* optimization finished, #iter = 771 nu = 0.123599 obj = -54.488238, rho = -0.146761 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) .*..* optimization finished, #iter = 375 nu = 0.103841 obj = -57.201530, rho = -0.127794 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -7.054967, rho = -0.424395 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 92% (92/100) (classification) Accuracy = 89.8% (898/1000) (classification) * optimization finished, #iter = 48 nu = 0.876280 obj = -8.302720, rho = -0.354388 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 97% (97/100) (classification) Accuracy = 94.7% (947/1000) (classification) * optimization finished, #iter = 51 nu = 0.836484 obj = -9.663598, rho = -0.273800 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 42 nu = 0.780000 obj = -11.090114, rho = -0.193677 nSV = 78, nBSV = 78 Total nSV = 78 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.706106 obj = -12.600884, rho = -0.152549 nSV = 73, nBSV = 69 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.632647 obj = -14.282973, rho = -0.141007 nSV = 66, nBSV = 60 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 66 nu = 0.562976 obj = -16.178823, rho = -0.121599 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 67 nu = 0.497321 obj = -18.307788, rho = -0.152285 nSV = 55, nBSV = 47 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 40 nu = 0.440000 obj = -20.831900, rho = -0.153388 nSV = 45, nBSV = 43 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 29 nu = 0.401327 obj = -23.576061, rho = -0.169805 nSV = 43, nBSV = 39 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 48 nu = 0.359948 obj = -26.425535, rho = -0.136107 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 64 nu = 0.316768 obj = -29.578627, rho = -0.179819 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 41 nu = 0.275998 obj = -33.070603, rho = -0.115453 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.243147 obj = -36.980768, rho = -0.057664 nSV = 28, nBSV = 18 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 81 nu = 0.210281 obj = -41.580659, rho = -0.035523 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 31 nu = 0.187667 obj = -46.906019, rho = -0.049350 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 34 nu = 0.165503 obj = -52.656306, rho = -0.061750 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 40 nu = 0.144263 obj = -59.356086, rho = -0.098749 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 68 nu = 0.131371 obj = -66.679131, rho = -0.100207 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 92 nu = 0.111763 obj = -74.920973, rho = -0.084355 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 46 nu = 0.909222 obj = -6.749875, rho = -0.284603 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 97% (97/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 50 nu = 0.849806 obj = -7.878065, rho = -0.289931 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 97% (97/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 47 nu = 0.793204 obj = -9.115695, rho = -0.282555 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 39 nu = 0.735315 obj = -10.468819, rho = -0.245514 nSV = 74, nBSV = 72 Total nSV = 74 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 44 nu = 0.672784 obj = -11.912939, rho = -0.228843 nSV = 69, nBSV = 64 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 47 nu = 0.594430 obj = -13.492595, rho = -0.225188 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 37 nu = 0.529731 obj = -15.319985, rho = -0.233056 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 68 nu = 0.483808 obj = -17.287902, rho = -0.233853 nSV = 51, nBSV = 44 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 69 nu = 0.416109 obj = -19.484346, rho = -0.234105 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 35 nu = 0.380000 obj = -22.008581, rho = -0.192340 nSV = 40, nBSV = 36 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 79 nu = 0.347733 obj = -24.341689, rho = -0.124634 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 87 nu = 0.297041 obj = -26.865290, rho = -0.100165 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.256235 obj = -29.666989, rho = -0.028888 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 40 nu = 0.221361 obj = -32.853733, rho = -0.007031 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 79 nu = 0.197195 obj = -36.249103, rho = 0.061936 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.171809 obj = -39.650300, rho = 0.131003 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 98 nu = 0.149617 obj = -43.100220, rho = 0.124051 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 146 nu = 0.129323 obj = -46.395951, rho = 0.121944 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*..* optimization finished, #iter = 476 nu = 0.108144 obj = -49.679065, rho = 0.128962 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 167 nu = 0.092024 obj = -53.409346, rho = 0.182588 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -6.712325, rho = -0.476503 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 93% (93/100) (classification) Accuracy = 90.1% (901/1000) (classification) * optimization finished, #iter = 42 nu = 0.836787 obj = -7.884110, rho = -0.394084 nSV = 84, nBSV = 82 Total nSV = 84 Accuracy = 97% (97/100) (classification) Accuracy = 93.5% (935/1000) (classification) * optimization finished, #iter = 42 nu = 0.792902 obj = -9.181625, rho = -0.351427 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 99% (99/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 48 nu = 0.734280 obj = -10.567227, rho = -0.321615 nSV = 76, nBSV = 72 Total nSV = 76 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 55 nu = 0.679584 obj = -12.038888, rho = -0.247454 nSV = 71, nBSV = 66 Total nSV = 71 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 58 nu = 0.603295 obj = -13.603455, rho = -0.209916 nSV = 65, nBSV = 57 Total nSV = 65 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.538055 obj = -15.399745, rho = -0.189940 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 38 nu = 0.481095 obj = -17.350491, rho = -0.215008 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 64 nu = 0.420584 obj = -19.538829, rho = -0.190557 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 60 nu = 0.376633 obj = -22.025656, rho = -0.226231 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 73 nu = 0.334521 obj = -24.693673, rho = -0.228882 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 54 nu = 0.297453 obj = -27.660107, rho = -0.193682 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 54 nu = 0.261424 obj = -30.896177, rho = -0.134467 nSV = 28, nBSV = 24 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 52 nu = 0.230374 obj = -34.439140, rho = -0.167357 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.201975 obj = -38.208405, rho = -0.158217 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.175060 obj = -42.348705, rho = -0.192268 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.149436 obj = -47.206667, rho = -0.186162 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 89 nu = 0.131813 obj = -52.864522, rho = -0.200525 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 86 nu = 0.115885 obj = -58.965067, rho = -0.248208 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 75 nu = 0.101039 obj = -65.996777, rho = -0.218346 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -6.671344, rho = -0.134805 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 97% (97/100) (classification) Accuracy = 94.4% (944/1000) (classification) * optimization finished, #iter = 46 nu = 0.825994 obj = -7.842488, rho = -0.175771 nSV = 85, nBSV = 81 Total nSV = 85 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 40 nu = 0.780000 obj = -9.152331, rho = -0.223587 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 44 nu = 0.723151 obj = -10.555928, rho = -0.246950 nSV = 74, nBSV = 71 Total nSV = 74 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 47 nu = 0.656021 obj = -12.131874, rho = -0.184952 nSV = 69, nBSV = 63 Total nSV = 69 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 42 nu = 0.606602 obj = -13.880236, rho = -0.124046 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 39 nu = 0.543518 obj = -15.766483, rho = -0.083052 nSV = 57, nBSV = 49 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.479429 obj = -17.949722, rho = -0.057154 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 38 nu = 0.438222 obj = -20.399399, rho = -0.026283 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 66 nu = 0.390715 obj = -23.040362, rho = -0.023195 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.346121 obj = -26.088351, rho = -0.075942 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 36 nu = 0.309881 obj = -29.342150, rho = -0.122062 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 31 nu = 0.280302 obj = -32.720317, rho = -0.230111 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 51 nu = 0.243468 obj = -36.384909, rho = -0.230422 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 54 nu = 0.211577 obj = -40.502122, rho = -0.240020 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 52 nu = 0.184898 obj = -45.131356, rho = -0.245945 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 72 nu = 0.164706 obj = -49.933534, rho = -0.276149 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 76 nu = 0.142295 obj = -55.116890, rho = -0.339372 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 93 nu = 0.123437 obj = -60.966953, rho = -0.329302 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*..* optimization finished, #iter = 315 nu = 0.107756 obj = -67.298691, rho = -0.292073 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -6.792516, rho = 0.027684 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 95% (95/100) (classification) Accuracy = 88.9% (889/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -8.032263, rho = -0.070298 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 96% (96/100) (classification) Accuracy = 94.4% (944/1000) (classification) * optimization finished, #iter = 43 nu = 0.801807 obj = -9.377138, rho = -0.130539 nSV = 82, nBSV = 79 Total nSV = 82 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 40 nu = 0.745233 obj = -10.798667, rho = -0.127137 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 36 nu = 0.684778 obj = -12.354421, rho = -0.200553 nSV = 70, nBSV = 67 Total nSV = 70 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 38 nu = 0.607726 obj = -14.103628, rho = -0.204073 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 50 nu = 0.544714 obj = -16.144014, rho = -0.240573 nSV = 57, nBSV = 51 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 36 nu = 0.489480 obj = -18.477068, rho = -0.209801 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 37 nu = 0.448296 obj = -21.021677, rho = -0.148300 nSV = 47, nBSV = 43 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 61 nu = 0.395800 obj = -23.828200, rho = -0.160733 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 55 nu = 0.352255 obj = -27.102397, rho = -0.200757 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 73 nu = 0.319063 obj = -30.711663, rho = -0.159062 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 32 nu = 0.290028 obj = -34.687367, rho = -0.098687 nSV = 31, nBSV = 27 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 36 nu = 0.258396 obj = -38.794903, rho = -0.159746 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 45 nu = 0.228855 obj = -43.062249, rho = -0.135997 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.197677 obj = -47.741206, rho = -0.113319 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 60 nu = 0.174590 obj = -52.855499, rho = -0.151163 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.152155 obj = -58.127485, rho = -0.152263 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 86 nu = 0.131674 obj = -63.887772, rho = -0.114461 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.119842 obj = -69.194486, rho = 0.098171 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -7.082844, rho = -0.186255 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 97% (97/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 45 nu = 0.891901 obj = -8.262205, rho = -0.184764 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 44 nu = 0.822560 obj = -9.573391, rho = -0.153622 nSV = 85, nBSV = 81 Total nSV = 85 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 46 nu = 0.759249 obj = -11.019819, rho = -0.091838 nSV = 78, nBSV = 73 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 43 nu = 0.688303 obj = -12.668234, rho = -0.078260 nSV = 73, nBSV = 68 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.625052 obj = -14.518251, rho = -0.047212 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 33 nu = 0.563320 obj = -16.556447, rho = -0.068956 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 42 nu = 0.510563 obj = -18.777331, rho = -0.087686 nSV = 53, nBSV = 49 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 29 nu = 0.453027 obj = -21.299083, rho = -0.134671 nSV = 46, nBSV = 43 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 70 nu = 0.405353 obj = -24.115935, rho = -0.067746 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 60 nu = 0.357323 obj = -27.335043, rho = -0.089458 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 60 nu = 0.326373 obj = -30.893536, rho = -0.077487 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 84 nu = 0.290368 obj = -34.556429, rho = -0.088748 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.259276 obj = -38.661630, rho = -0.177377 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.228420 obj = -42.744194, rho = -0.232573 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) ..*.* optimization finished, #iter = 315 nu = 0.193441 obj = -47.436483, rho = -0.245820 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 144 nu = 0.168319 obj = -53.207849, rho = -0.235200 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 225 nu = 0.151932 obj = -58.947371, rho = -0.234075 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*...*...* optimization finished, #iter = 610 nu = 0.129911 obj = -65.412679, rho = -0.272618 nSV = 19, nBSV = 8 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 236 nu = 0.110482 obj = -73.265800, rho = -0.256500 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 49 nu = 0.909441 obj = -6.707481, rho = 0.069260 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 52 nu = 0.873544 obj = -7.769371, rho = -0.101169 nSV = 89, nBSV = 85 Total nSV = 89 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.812264 obj = -8.871279, rho = -0.098195 nSV = 82, nBSV = 78 Total nSV = 82 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 51 nu = 0.735005 obj = -10.000654, rho = -0.092110 nSV = 76, nBSV = 71 Total nSV = 76 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 43 nu = 0.650626 obj = -11.214178, rho = -0.088430 nSV = 67, nBSV = 62 Total nSV = 67 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 39 nu = 0.578288 obj = -12.514727, rho = -0.026551 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 44 nu = 0.515736 obj = -13.880297, rho = 0.014640 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 59 nu = 0.444386 obj = -15.305717, rho = -0.022189 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 45 nu = 0.387591 obj = -16.897623, rho = -0.038887 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 42 nu = 0.335848 obj = -18.589883, rho = -0.058489 nSV = 38, nBSV = 30 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 92 nu = 0.287386 obj = -20.529092, rho = -0.066739 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 68 nu = 0.251593 obj = -22.680722, rho = -0.014152 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 98 nu = 0.220560 obj = -25.004105, rho = -0.001518 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 93 nu = 0.193832 obj = -27.133413, rho = 0.046027 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.167487 obj = -29.202599, rho = 0.034416 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 89 nu = 0.143283 obj = -31.220139, rho = 0.045516 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 65 nu = 0.121377 obj = -33.200968, rho = 0.042910 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 96 nu = 0.102221 obj = -34.871494, rho = 0.024987 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 156 nu = 0.083881 obj = -36.683362, rho = 0.039383 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.068701 obj = -38.653713, rho = 0.079921 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 50 nu = 0.930705 obj = -6.799940, rho = -0.174250 nSV = 94, nBSV = 91 Total nSV = 94 Accuracy = 98% (98/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -7.910631, rho = -0.136595 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 42 nu = 0.801773 obj = -9.142384, rho = -0.163776 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 43 nu = 0.740000 obj = -10.470945, rho = -0.265226 nSV = 76, nBSV = 72 Total nSV = 76 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 41 nu = 0.663648 obj = -11.911056, rho = -0.281215 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 35 nu = 0.605960 obj = -13.443287, rho = -0.254451 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 35 nu = 0.542820 obj = -15.075189, rho = -0.285383 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 40 nu = 0.476828 obj = -16.848275, rho = -0.332988 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.418022 obj = -18.814728, rho = -0.317668 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 33 nu = 0.366389 obj = -20.959629, rho = -0.327964 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.324074 obj = -23.359066, rho = -0.336321 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 37 nu = 0.279313 obj = -25.967056, rho = -0.335876 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 43 nu = 0.243820 obj = -28.928876, rho = -0.330620 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 68 nu = 0.212764 obj = -32.389282, rho = -0.303965 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 53 nu = 0.186215 obj = -36.293199, rho = -0.309454 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.166373 obj = -40.658013, rho = -0.225167 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 59 nu = 0.144642 obj = -45.453918, rho = -0.230528 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 81 nu = 0.128853 obj = -50.799916, rho = -0.193973 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 183 nu = 0.110201 obj = -56.711097, rho = -0.167401 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 203 nu = 0.096842 obj = -63.441346, rho = -0.162823 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.860000 obj = -6.525518, rho = -0.418353 nSV = 88, nBSV = 84 Total nSV = 88 Accuracy = 91% (91/100) (classification) Accuracy = 91% (910/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -7.656474, rho = -0.340705 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 96% (96/100) (classification) Accuracy = 94.7% (947/1000) (classification) * optimization finished, #iter = 42 nu = 0.779215 obj = -8.865590, rho = -0.250596 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 42 nu = 0.716154 obj = -10.160703, rho = -0.214557 nSV = 73, nBSV = 70 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 43 nu = 0.637417 obj = -11.600073, rho = -0.205042 nSV = 66, nBSV = 61 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 38 nu = 0.569743 obj = -13.275722, rho = -0.239232 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 38 nu = 0.508457 obj = -15.225823, rho = -0.211487 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 38 nu = 0.471757 obj = -17.361849, rho = -0.152636 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 59 nu = 0.422608 obj = -19.630143, rho = -0.189406 nSV = 47, nBSV = 38 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 77 nu = 0.368894 obj = -22.260252, rho = -0.226017 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 38 nu = 0.330701 obj = -25.351888, rho = -0.166881 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 48 nu = 0.303441 obj = -28.605492, rho = -0.159535 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 31 nu = 0.272127 obj = -32.047930, rho = -0.076012 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 37 nu = 0.238948 obj = -35.701705, rho = -0.139295 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 76 nu = 0.207925 obj = -39.691574, rho = -0.187954 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.180137 obj = -44.195512, rho = -0.205155 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 91 nu = 0.155720 obj = -49.519570, rho = -0.228928 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 95 nu = 0.135046 obj = -55.891495, rho = -0.251225 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 97 nu = 0.121636 obj = -63.154606, rho = -0.389098 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.104943 obj = -71.348773, rho = -0.432852 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -6.618771, rho = -0.372644 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 94% (94/100) (classification) Accuracy = 90.6% (906/1000) (classification) * optimization finished, #iter = 51 nu = 0.856349 obj = -7.650155, rho = -0.273713 nSV = 88, nBSV = 84 Total nSV = 88 Accuracy = 99% (99/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 42 nu = 0.780000 obj = -8.769121, rho = -0.236047 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 38 nu = 0.719617 obj = -10.010796, rho = -0.295855 nSV = 72, nBSV = 70 Total nSV = 72 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 40 nu = 0.632193 obj = -11.346514, rho = -0.291103 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 39 nu = 0.565293 obj = -12.886136, rho = -0.258616 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 37 nu = 0.512593 obj = -14.574500, rho = -0.274821 nSV = 53, nBSV = 50 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 41 nu = 0.463144 obj = -16.297506, rho = -0.264524 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 33 nu = 0.406504 obj = -18.173154, rho = -0.306328 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.360185 obj = -20.166915, rho = -0.287732 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 86 nu = 0.309327 obj = -22.397599, rho = -0.264142 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 42 nu = 0.266768 obj = -25.009660, rho = -0.261294 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.239028 obj = -27.811204, rho = -0.225032 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 99 nu = 0.209072 obj = -30.845610, rho = -0.287498 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 42 nu = 0.183199 obj = -34.001795, rho = -0.268370 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.155410 obj = -37.538466, rho = -0.265568 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 57 nu = 0.136002 obj = -41.638581, rho = -0.298788 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.119517 obj = -45.866218, rho = -0.409556 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 193 nu = 0.102781 obj = -50.496239, rho = -0.450725 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.088715 obj = -55.712163, rho = -0.472821 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -7.004354, rho = 0.190696 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 91% (91/100) (classification) Accuracy = 89.9% (899/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -8.289188, rho = 0.079497 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 96% (96/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 46 nu = 0.834920 obj = -9.682317, rho = -0.086908 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 43 nu = 0.771151 obj = -11.177137, rho = -0.163852 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 49 nu = 0.703649 obj = -12.771180, rho = -0.125337 nSV = 73, nBSV = 68 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 54 nu = 0.636422 obj = -14.557330, rho = -0.050743 nSV = 68, nBSV = 60 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 44 nu = 0.574544 obj = -16.543659, rho = -0.004953 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 29 nu = 0.515796 obj = -18.727461, rho = -0.007254 nSV = 53, nBSV = 50 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 61 nu = 0.452528 obj = -21.161040, rho = -0.004635 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 44 nu = 0.397962 obj = -24.029929, rho = -0.016521 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 33 nu = 0.360000 obj = -27.289204, rho = -0.014130 nSV = 38, nBSV = 34 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 72 nu = 0.321639 obj = -30.799080, rho = 0.005159 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 89 nu = 0.281131 obj = -34.864341, rho = 0.062684 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 53 nu = 0.250398 obj = -39.681663, rho = 0.128325 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 45 nu = 0.230261 obj = -44.822600, rho = 0.180445 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 60 nu = 0.209394 obj = -50.014265, rho = 0.109618 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 66 nu = 0.183495 obj = -55.022549, rho = 0.118279 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *..* optimization finished, #iter = 204 nu = 0.159600 obj = -60.218989, rho = 0.227702 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) ..*.* optimization finished, #iter = 331 nu = 0.134281 obj = -66.168646, rho = 0.253874 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 235 nu = 0.116989 obj = -73.098167, rho = 0.292783 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 43 nu = 0.780000 obj = -6.447987, rho = -0.622817 nSV = 81, nBSV = 77 Total nSV = 81 Accuracy = 76% (76/100) (classification) Accuracy = 70.2% (702/1000) (classification) * optimization finished, #iter = 43 nu = 0.780000 obj = -7.743977, rho = -0.519365 nSV = 81, nBSV = 77 Total nSV = 81 Accuracy = 89% (89/100) (classification) Accuracy = 86.1% (861/1000) (classification) * optimization finished, #iter = 49 nu = 0.774790 obj = -9.101389, rho = -0.404189 nSV = 80, nBSV = 76 Total nSV = 80 Accuracy = 98% (98/100) (classification) Accuracy = 94.8% (948/1000) (classification) * optimization finished, #iter = 43 nu = 0.720000 obj = -10.538688, rho = -0.390609 nSV = 73, nBSV = 69 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 37 nu = 0.669463 obj = -12.088118, rho = -0.318155 nSV = 69, nBSV = 66 Total nSV = 69 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 38 nu = 0.613597 obj = -13.706075, rho = -0.331405 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 39 nu = 0.543814 obj = -15.479258, rho = -0.359130 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 48 nu = 0.488093 obj = -17.415192, rho = -0.344030 nSV = 51, nBSV = 44 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 64 nu = 0.431646 obj = -19.510795, rho = -0.352231 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 43 nu = 0.381405 obj = -21.830158, rho = -0.317572 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 64 nu = 0.336056 obj = -24.186848, rho = -0.267853 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.293346 obj = -26.796462, rho = -0.281093 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 155 nu = 0.250281 obj = -29.856555, rho = -0.263523 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 68 nu = 0.219511 obj = -33.466956, rho = -0.245107 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 63 nu = 0.194819 obj = -37.362331, rho = -0.228542 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 32 nu = 0.176629 obj = -41.416880, rho = -0.302672 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 83 nu = 0.148805 obj = -45.640926, rho = -0.310102 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 55 nu = 0.129961 obj = -50.579908, rho = -0.351551 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 68 nu = 0.113487 obj = -55.975977, rho = -0.449311 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 67 nu = 0.100737 obj = -61.349538, rho = -0.623829 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -7.561058, rho = 0.245710 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 95% (95/100) (classification) Accuracy = 92.8% (928/1000) (classification) * optimization finished, #iter = 52 nu = 0.941215 obj = -8.929947, rho = 0.123229 nSV = 96, nBSV = 93 Total nSV = 96 Accuracy = 96% (96/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 45 nu = 0.893958 obj = -10.404808, rho = 0.077486 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 96% (96/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 52 nu = 0.826614 obj = -12.004148, rho = 0.057023 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 96% (96/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 51 nu = 0.758437 obj = -13.750414, rho = -0.013421 nSV = 78, nBSV = 73 Total nSV = 78 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 41 nu = 0.681546 obj = -15.720704, rho = -0.024071 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 46 nu = 0.615138 obj = -17.869317, rho = 0.007623 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 31 nu = 0.549011 obj = -20.298883, rho = 0.022752 nSV = 57, nBSV = 54 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 54 nu = 0.489007 obj = -23.037745, rho = -0.039354 nSV = 52, nBSV = 45 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 38 nu = 0.440432 obj = -26.164409, rho = -0.051699 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.399090 obj = -29.457487, rho = -0.003467 nSV = 44, nBSV = 36 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 59 nu = 0.348440 obj = -33.098104, rho = -0.022581 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.305066 obj = -37.250440, rho = -0.034209 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 87 nu = 0.274158 obj = -41.952435, rho = -0.016027 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.241574 obj = -46.912002, rho = -0.021182 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 56 nu = 0.210429 obj = -52.741160, rho = -0.050069 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 72 nu = 0.189640 obj = -58.971637, rho = -0.023383 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.169264 obj = -65.389389, rho = 0.004245 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.143834 obj = -72.571580, rho = -0.001747 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*..* optimization finished, #iter = 313 nu = 0.126010 obj = -80.616508, rho = -0.043257 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.887032 obj = -6.653603, rho = -0.050877 nSV = 90, nBSV = 86 Total nSV = 90 Accuracy = 93% (93/100) (classification) Accuracy = 92% (920/1000) (classification) * optimization finished, #iter = 45 nu = 0.830682 obj = -7.803594, rho = -0.130128 nSV = 85, nBSV = 81 Total nSV = 85 Accuracy = 96% (96/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 46 nu = 0.763218 obj = -9.131031, rho = -0.179110 nSV = 79, nBSV = 75 Total nSV = 79 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 38 nu = 0.720000 obj = -10.615969, rho = -0.130017 nSV = 73, nBSV = 71 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 49 nu = 0.670515 obj = -12.200195, rho = -0.070400 nSV = 68, nBSV = 63 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 41 nu = 0.607147 obj = -13.930511, rho = -0.070717 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 45 nu = 0.542877 obj = -15.854986, rho = -0.103363 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 39 nu = 0.490384 obj = -17.975706, rho = -0.163569 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 32 nu = 0.438812 obj = -20.308712, rho = -0.168399 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 44 nu = 0.396104 obj = -22.788241, rho = -0.140076 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 66 nu = 0.351346 obj = -25.406015, rho = -0.127056 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 67 nu = 0.305776 obj = -28.280500, rho = -0.120680 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.271819 obj = -31.380112, rho = -0.095169 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 47 nu = 0.242178 obj = -34.461381, rho = -0.137954 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 91 nu = 0.210387 obj = -37.367093, rho = -0.267142 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.176159 obj = -40.578261, rho = -0.263534 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..* optimization finished, #iter = 241 nu = 0.152523 obj = -43.979556, rho = -0.332550 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..*.* optimization finished, #iter = 338 nu = 0.128701 obj = -47.581311, rho = -0.375586 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*..* optimization finished, #iter = 337 nu = 0.110659 obj = -51.340673, rho = -0.462860 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 211 nu = 0.095656 obj = -55.298753, rho = -0.480011 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -6.923881, rho = 0.282010 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 84% (84/100) (classification) Accuracy = 83.8% (838/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -8.237122, rho = 0.085083 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 95% (95/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 43 nu = 0.808222 obj = -9.665422, rho = 0.118183 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 40 nu = 0.750918 obj = -11.249711, rho = 0.087581 nSV = 77, nBSV = 74 Total nSV = 77 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 52 nu = 0.698035 obj = -13.017089, rho = 0.054484 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 36 nu = 0.626263 obj = -15.033744, rho = 0.036447 nSV = 65, nBSV = 62 Total nSV = 65 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 30 nu = 0.580000 obj = -17.323064, rho = 0.011263 nSV = 58, nBSV = 58 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 36 nu = 0.530869 obj = -19.747425, rho = 0.001335 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 40 nu = 0.475677 obj = -22.440659, rho = -0.027742 nSV = 52, nBSV = 44 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 52 nu = 0.427281 obj = -25.513237, rho = 0.045675 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 51 nu = 0.378902 obj = -28.908865, rho = 0.065673 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 94 nu = 0.341481 obj = -32.688734, rho = 0.103802 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 95 nu = 0.302902 obj = -36.806146, rho = 0.157258 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.263846 obj = -41.538999, rho = 0.208546 nSV = 32, nBSV = 22 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.232146 obj = -47.166486, rho = 0.238511 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 174 nu = 0.203360 obj = -53.931208, rho = 0.238019 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 56 nu = 0.180737 obj = -62.037787, rho = 0.255409 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 65 nu = 0.164094 obj = -71.773471, rho = 0.302918 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 77 nu = 0.152260 obj = -82.550627, rho = 0.344180 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 252 nu = 0.140987 obj = -93.563541, rho = 0.369680 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 56 nu = 0.896368 obj = -6.913463, rho = -0.326232 nSV = 92, nBSV = 87 Total nSV = 92 Accuracy = 96% (96/100) (classification) Accuracy = 94.3% (943/1000) (classification) * optimization finished, #iter = 52 nu = 0.855611 obj = -8.183533, rho = -0.271927 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 48 nu = 0.800000 obj = -9.591564, rho = -0.200563 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 43 nu = 0.754234 obj = -11.177746, rho = -0.137357 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 43 nu = 0.686899 obj = -12.913748, rho = -0.092622 nSV = 71, nBSV = 66 Total nSV = 71 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 41 nu = 0.629148 obj = -14.909568, rho = -0.046884 nSV = 65, nBSV = 62 Total nSV = 65 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 33 nu = 0.580374 obj = -17.065950, rho = -0.057156 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 49 nu = 0.524463 obj = -19.400248, rho = -0.115319 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 59 nu = 0.465445 obj = -22.087421, rho = -0.126725 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.418744 obj = -25.073097, rho = -0.175225 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 37 nu = 0.370814 obj = -28.498365, rho = -0.172334 nSV = 39, nBSV = 36 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 51 nu = 0.331008 obj = -32.316470, rho = -0.242146 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 44 nu = 0.295107 obj = -36.639741, rho = -0.265557 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 47 nu = 0.266990 obj = -41.420134, rho = -0.171784 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 58 nu = 0.235631 obj = -46.728913, rho = -0.168747 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 51 nu = 0.208079 obj = -52.643246, rho = -0.154376 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 94 nu = 0.182212 obj = -59.654893, rho = -0.174595 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 180 nu = 0.163091 obj = -67.732347, rho = -0.124278 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) ...* optimization finished, #iter = 347 nu = 0.141378 obj = -77.256780, rho = -0.095590 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 180 nu = 0.126046 obj = -88.803247, rho = -0.031062 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.838616 obj = -6.406631, rho = -0.485501 nSV = 84, nBSV = 82 Total nSV = 84 Accuracy = 92% (92/100) (classification) Accuracy = 86.7% (867/1000) (classification) * optimization finished, #iter = 44 nu = 0.788318 obj = -7.551983, rho = -0.456006 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 95% (95/100) (classification) Accuracy = 90% (900/1000) (classification) * optimization finished, #iter = 48 nu = 0.735864 obj = -8.856471, rho = -0.449403 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 97% (97/100) (classification) Accuracy = 92% (920/1000) (classification) * optimization finished, #iter = 44 nu = 0.682937 obj = -10.346828, rho = -0.406496 nSV = 70, nBSV = 67 Total nSV = 70 Accuracy = 95% (95/100) (classification) Accuracy = 93.4% (934/1000) (classification) * optimization finished, #iter = 44 nu = 0.624285 obj = -12.058605, rho = -0.361604 nSV = 66, nBSV = 60 Total nSV = 66 Accuracy = 95% (95/100) (classification) Accuracy = 94.3% (943/1000) (classification) * optimization finished, #iter = 37 nu = 0.577295 obj = -14.062672, rho = -0.389716 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 96% (96/100) (classification) Accuracy = 94.1% (941/1000) (classification) * optimization finished, #iter = 41 nu = 0.521741 obj = -16.400619, rho = -0.424226 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 96% (96/100) (classification) Accuracy = 94.2% (942/1000) (classification) * optimization finished, #iter = 38 nu = 0.475317 obj = -19.184507, rho = -0.423521 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 96% (96/100) (classification) Accuracy = 94.7% (947/1000) (classification) * optimization finished, #iter = 28 nu = 0.444261 obj = -22.331378, rho = -0.362243 nSV = 46, nBSV = 43 Total nSV = 46 Accuracy = 95% (95/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 34 nu = 0.400763 obj = -25.955541, rho = -0.365644 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 95% (95/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 72 nu = 0.374338 obj = -29.986423, rho = -0.309987 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 96% (96/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 38 nu = 0.344521 obj = -34.484939, rho = -0.373324 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 97% (97/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 64 nu = 0.311035 obj = -39.470315, rho = -0.409691 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 93 nu = 0.280000 obj = -45.281719, rho = -0.366926 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 77 nu = 0.250641 obj = -51.593788, rho = -0.341792 nSV = 30, nBSV = 21 Total nSV = 30 Accuracy = 96% (96/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.226804 obj = -59.076035, rho = -0.316699 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 65 nu = 0.208324 obj = -67.312090, rho = -0.352531 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 92 nu = 0.186928 obj = -76.141265, rho = -0.458504 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) *..* optimization finished, #iter = 203 nu = 0.165463 obj = -85.508306, rho = -0.557781 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) ...*.* optimization finished, #iter = 471 nu = 0.146545 obj = -95.813264, rho = -0.598024 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.937162 obj = -7.151930, rho = -0.127041 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 96% (96/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 48 nu = 0.888952 obj = -8.431412, rho = -0.128725 nSV = 90, nBSV = 86 Total nSV = 90 Accuracy = 96% (96/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 48 nu = 0.826203 obj = -9.859592, rho = -0.126640 nSV = 85, nBSV = 81 Total nSV = 85 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.767162 obj = -11.488366, rho = -0.106768 nSV = 78, nBSV = 76 Total nSV = 78 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 37 nu = 0.700000 obj = -13.346713, rho = -0.108660 nSV = 71, nBSV = 69 Total nSV = 71 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 40 nu = 0.651107 obj = -15.440392, rho = -0.103820 nSV = 66, nBSV = 63 Total nSV = 66 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 47 nu = 0.594949 obj = -17.755051, rho = -0.112222 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 37 nu = 0.538342 obj = -20.375194, rho = -0.106477 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 35 nu = 0.488945 obj = -23.233338, rho = -0.151838 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 39 nu = 0.445906 obj = -26.356276, rho = -0.075199 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 58 nu = 0.396771 obj = -29.736164, rho = -0.076092 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 56 nu = 0.352205 obj = -33.570727, rho = -0.111534 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 75 nu = 0.308526 obj = -37.772450, rho = -0.136832 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 73 nu = 0.277653 obj = -42.491253, rho = -0.224077 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 149 nu = 0.241654 obj = -47.790721, rho = -0.191437 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.212193 obj = -53.950372, rho = -0.163036 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.185762 obj = -61.189549, rho = -0.135774 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .**.* optimization finished, #iter = 164 nu = 0.170909 obj = -69.334030, rho = -0.285567 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .**.* optimization finished, #iter = 199 nu = 0.146814 obj = -78.317874, rho = -0.278052 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.128113 obj = -89.318112, rho = -0.299573 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 48 nu = 0.931943 obj = -7.032448, rho = -0.026985 nSV = 94, nBSV = 91 Total nSV = 94 Accuracy = 90% (90/100) (classification) Accuracy = 94.8% (948/1000) (classification) * optimization finished, #iter = 47 nu = 0.872231 obj = -8.262053, rho = -0.050329 nSV = 89, nBSV = 85 Total nSV = 89 Accuracy = 93% (93/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 45 nu = 0.820925 obj = -9.653761, rho = -0.078026 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 46 nu = 0.764877 obj = -11.158775, rho = -0.103774 nSV = 79, nBSV = 75 Total nSV = 79 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 46 nu = 0.692785 obj = -12.840173, rho = -0.129929 nSV = 72, nBSV = 67 Total nSV = 72 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 38 nu = 0.631932 obj = -14.752701, rho = -0.141616 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 47 nu = 0.568550 obj = -16.897902, rho = -0.162973 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 44 nu = 0.517075 obj = -19.318989, rho = -0.148429 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 29 nu = 0.470602 obj = -21.978411, rho = -0.205981 nSV = 48, nBSV = 46 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 62 nu = 0.422309 obj = -24.763516, rho = -0.148124 nSV = 47, nBSV = 39 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 37 nu = 0.371766 obj = -27.999926, rho = -0.176419 nSV = 39, nBSV = 35 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 49 nu = 0.331334 obj = -31.611198, rho = -0.172362 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 45 nu = 0.297026 obj = -35.588997, rho = -0.135558 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 53 nu = 0.265376 obj = -39.756209, rho = -0.190114 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 96 nu = 0.231398 obj = -44.210537, rho = -0.203261 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 87 nu = 0.205077 obj = -49.207827, rho = -0.169796 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 85 nu = 0.176241 obj = -54.510551, rho = -0.190492 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 141 nu = 0.154677 obj = -60.645308, rho = -0.171307 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) ..* optimization finished, #iter = 295 nu = 0.134402 obj = -67.050432, rho = -0.145733 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 175 nu = 0.114209 obj = -74.725216, rho = -0.149273 nSV = 19, nBSV = 8 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 47 nu = 0.893432 obj = -6.639570, rho = -0.061369 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 91% (91/100) (classification) Accuracy = 94.6% (946/1000) (classification) * optimization finished, #iter = 51 nu = 0.838533 obj = -7.739476, rho = -0.140265 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 96% (96/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 44 nu = 0.765210 obj = -8.988347, rho = -0.135056 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 40 nu = 0.709386 obj = -10.386646, rho = -0.140436 nSV = 73, nBSV = 69 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 34 nu = 0.644371 obj = -11.978818, rho = -0.099217 nSV = 66, nBSV = 64 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 38 nu = 0.589330 obj = -13.718089, rho = -0.074625 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 35 nu = 0.522615 obj = -15.758759, rho = -0.045772 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 54 nu = 0.471177 obj = -18.154634, rho = -0.067830 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 58 nu = 0.426237 obj = -20.923930, rho = -0.054047 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 42 nu = 0.388683 obj = -24.061703, rho = 0.000019 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 26 nu = 0.351532 obj = -27.620485, rho = 0.040380 nSV = 37, nBSV = 33 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 31 nu = 0.319151 obj = -31.532406, rho = 0.013713 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 56 nu = 0.290837 obj = -35.946111, rho = 0.042898 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 59 nu = 0.256662 obj = -40.808432, rho = 0.048659 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.232470 obj = -46.337295, rho = 0.101059 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 64 nu = 0.209449 obj = -52.300903, rho = 0.085695 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 165 nu = 0.184127 obj = -58.745286, rho = 0.096430 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 61 nu = 0.160327 obj = -66.251817, rho = 0.098644 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 62 nu = 0.141921 obj = -75.088808, rho = 0.124076 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 60 nu = 0.129090 obj = -84.553787, rho = 0.183008 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.920000 obj = -6.794953, rho = -0.342450 nSV = 94, nBSV = 91 Total nSV = 94 Accuracy = 96% (96/100) (classification) Accuracy = 93% (930/1000) (classification) * optimization finished, #iter = 47 nu = 0.876780 obj = -7.854116, rho = -0.254989 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 44 nu = 0.796795 obj = -9.035854, rho = -0.236895 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 41 nu = 0.743835 obj = -10.307079, rho = -0.241113 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 37 nu = 0.661259 obj = -11.637984, rho = -0.239484 nSV = 68, nBSV = 66 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 41 nu = 0.593518 obj = -13.073355, rho = -0.268154 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 55 nu = 0.529497 obj = -14.597264, rho = -0.241803 nSV = 56, nBSV = 50 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 42 nu = 0.461986 obj = -16.279274, rho = -0.282360 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 53 nu = 0.402820 obj = -18.161440, rho = -0.289724 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 67 nu = 0.349309 obj = -20.353737, rho = -0.328000 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.307243 obj = -22.886155, rho = -0.306099 nSV = 35, nBSV = 26 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.265070 obj = -25.865392, rho = -0.310997 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 95 nu = 0.232906 obj = -29.443845, rho = -0.324495 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 49 nu = 0.206368 obj = -33.736463, rho = -0.289382 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 52 nu = 0.187984 obj = -38.637411, rho = -0.356453 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 82 nu = 0.166260 obj = -44.322721, rho = -0.398255 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 83 nu = 0.149706 obj = -51.008064, rho = -0.452348 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 44 nu = 0.137656 obj = -58.512144, rho = -0.573911 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 55 nu = 0.127295 obj = -66.534902, rho = -0.919197 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 95.2% (952/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.114677 obj = -74.520186, rho = -1.126489 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -6.594750, rho = -0.130953 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 93% (93/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -7.731764, rho = -0.197593 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 95% (95/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 42 nu = 0.760000 obj = -9.041557, rho = -0.175274 nSV = 77, nBSV = 74 Total nSV = 77 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 37 nu = 0.702616 obj = -10.529302, rho = -0.139432 nSV = 72, nBSV = 70 Total nSV = 72 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 34 nu = 0.660000 obj = -12.175443, rho = -0.086986 nSV = 66, nBSV = 66 Total nSV = 66 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 40 nu = 0.612183 obj = -13.900348, rho = -0.032435 nSV = 63, nBSV = 57 Total nSV = 63 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 42 nu = 0.540000 obj = -15.824069, rho = -0.069713 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 93 nu = 0.486382 obj = -17.959743, rho = -0.074077 nSV = 52, nBSV = 44 Total nSV = 52 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 66 nu = 0.428820 obj = -20.440697, rho = -0.103410 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 40 nu = 0.389540 obj = -23.267027, rho = -0.163514 nSV = 41, nBSV = 37 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 36 nu = 0.358725 obj = -26.231425, rho = -0.159323 nSV = 37, nBSV = 33 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 59 nu = 0.313343 obj = -29.386262, rho = -0.189465 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 59 nu = 0.276004 obj = -32.833465, rho = -0.170514 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 69 nu = 0.241735 obj = -36.716729, rho = -0.173729 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 36 nu = 0.211861 obj = -41.082818, rho = -0.250415 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.190470 obj = -45.941089, rho = -0.303190 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.170942 obj = -50.460506, rho = -0.169348 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 99 nu = 0.148169 obj = -54.900964, rho = -0.159807 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 62 nu = 0.124737 obj = -59.673697, rho = -0.148955 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 75 nu = 0.105118 obj = -65.323069, rho = -0.157373 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -7.061157, rho = -0.120810 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -8.264669, rho = -0.161033 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 51 nu = 0.824956 obj = -9.603609, rho = -0.160984 nSV = 85, nBSV = 81 Total nSV = 85 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 56 nu = 0.757897 obj = -11.078131, rho = -0.107538 nSV = 79, nBSV = 74 Total nSV = 79 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 41 nu = 0.679593 obj = -12.791372, rho = -0.140695 nSV = 69, nBSV = 66 Total nSV = 69 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.621054 obj = -14.801206, rho = -0.177714 nSV = 65, nBSV = 59 Total nSV = 65 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 29 nu = 0.569133 obj = -17.035188, rho = -0.237111 nSV = 58, nBSV = 56 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.515816 obj = -19.493567, rho = -0.246482 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 41 nu = 0.461556 obj = -22.305554, rho = -0.218606 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 61 nu = 0.411976 obj = -25.598820, rho = -0.198004 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 57 nu = 0.367454 obj = -29.475717, rho = -0.220348 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 56 nu = 0.338298 obj = -33.933189, rho = -0.205852 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 42 nu = 0.307397 obj = -38.832110, rho = -0.139711 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 81 nu = 0.280346 obj = -44.137921, rho = -0.176386 nSV = 32, nBSV = 23 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 98 nu = 0.248991 obj = -50.159701, rho = -0.219216 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 71 nu = 0.226001 obj = -56.948515, rho = -0.253856 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 49 nu = 0.195954 obj = -64.383118, rho = -0.319025 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 86 nu = 0.182341 obj = -72.357317, rho = -0.562067 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 151 nu = 0.156278 obj = -80.953558, rho = -0.610579 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) ..*.* optimization finished, #iter = 337 nu = 0.136854 obj = -91.112227, rho = -0.676216 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.939453 obj = -7.086324, rho = -0.270400 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 47 nu = 0.893167 obj = -8.309115, rho = -0.296348 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 52 nu = 0.835984 obj = -9.636974, rho = -0.243511 nSV = 86, nBSV = 81 Total nSV = 86 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 58 nu = 0.776881 obj = -11.075586, rho = -0.175056 nSV = 81, nBSV = 74 Total nSV = 81 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 43 nu = 0.703331 obj = -12.654206, rho = -0.145708 nSV = 73, nBSV = 70 Total nSV = 73 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 51 nu = 0.621797 obj = -14.443039, rho = -0.129443 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 52 nu = 0.563496 obj = -16.463739, rho = -0.105498 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 100% (100/100) (classification) Accuracy = 99.3% (993/1000) (classification) * optimization finished, #iter = 33 nu = 0.505876 obj = -18.712594, rho = -0.095821 nSV = 53, nBSV = 49 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 99.3% (993/1000) (classification) * optimization finished, #iter = 36 nu = 0.449866 obj = -21.235989, rho = -0.085151 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 99.3% (993/1000) (classification) * optimization finished, #iter = 56 nu = 0.399134 obj = -24.129062, rho = -0.075428 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 33 nu = 0.360636 obj = -27.442820, rho = -0.051835 nSV = 38, nBSV = 34 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.320666 obj = -31.063515, rho = 0.004824 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 32 nu = 0.288130 obj = -35.158912, rho = -0.061390 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 36 nu = 0.252600 obj = -39.671346, rho = -0.055250 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.227539 obj = -44.586454, rho = -0.121089 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 41 nu = 0.197741 obj = -50.350845, rho = -0.040424 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 86 nu = 0.179503 obj = -56.532852, rho = 0.142685 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 144 nu = 0.159227 obj = -63.165462, rho = 0.146838 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 132 nu = 0.141010 obj = -70.170278, rho = 0.074861 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 57 nu = 0.120596 obj = -77.855252, rho = -0.010677 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -6.828935, rho = -0.315330 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 97% (97/100) (classification) Accuracy = 94.6% (946/1000) (classification) * optimization finished, #iter = 48 nu = 0.867189 obj = -7.949340, rho = -0.270598 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 97% (97/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 41 nu = 0.807653 obj = -9.175853, rho = -0.244488 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 45 nu = 0.725499 obj = -10.551576, rho = -0.229458 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 38 nu = 0.677271 obj = -12.080113, rho = -0.159899 nSV = 68, nBSV = 66 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 48 nu = 0.607707 obj = -13.685067, rho = -0.164056 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.539462 obj = -15.484273, rho = -0.120459 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 28 nu = 0.480745 obj = -17.519343, rho = -0.073294 nSV = 50, nBSV = 47 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 43 nu = 0.430675 obj = -19.683828, rho = -0.100995 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 36 nu = 0.383794 obj = -22.054952, rho = -0.156799 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 40 nu = 0.332236 obj = -24.696451, rho = -0.152865 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 36 nu = 0.295212 obj = -27.771419, rho = -0.170657 nSV = 31, nBSV = 28 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 63 nu = 0.261958 obj = -30.989726, rho = -0.196091 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 84 nu = 0.228349 obj = -34.556286, rho = -0.206801 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 62 nu = 0.201987 obj = -38.600716, rho = -0.243820 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 73 nu = 0.175128 obj = -42.964810, rho = -0.252769 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 69 nu = 0.151823 obj = -48.049844, rho = -0.215784 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 62 nu = 0.136994 obj = -53.663558, rho = -0.205009 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.118026 obj = -59.586848, rho = -0.162155 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.101702 obj = -66.604726, rho = -0.127163 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 51 nu = 0.887389 obj = -6.570869, rho = -0.177235 nSV = 90, nBSV = 86 Total nSV = 90 Accuracy = 97% (97/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 43 nu = 0.835758 obj = -7.654915, rho = -0.228240 nSV = 84, nBSV = 82 Total nSV = 84 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 45 nu = 0.767518 obj = -8.856052, rho = -0.259318 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.700000 obj = -10.197062, rho = -0.188377 nSV = 72, nBSV = 69 Total nSV = 72 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 60 nu = 0.638540 obj = -11.679883, rho = -0.148309 nSV = 67, nBSV = 62 Total nSV = 67 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 35 nu = 0.568444 obj = -13.397923, rho = -0.153199 nSV = 59, nBSV = 56 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 30 nu = 0.526753 obj = -15.317931, rho = -0.271212 nSV = 54, nBSV = 52 Total nSV = 54 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 33 nu = 0.468475 obj = -17.389904, rho = -0.259913 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 38 nu = 0.411102 obj = -19.856230, rho = -0.276935 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 47 nu = 0.374937 obj = -22.695543, rho = -0.255276 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 55 nu = 0.333306 obj = -25.781717, rho = -0.216244 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 65 nu = 0.295302 obj = -29.456986, rho = -0.213715 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 99.3% (993/1000) (classification) * optimization finished, #iter = 45 nu = 0.266295 obj = -33.703433, rho = -0.184688 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 62 nu = 0.239245 obj = -38.474806, rho = -0.171933 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 27 nu = 0.217746 obj = -43.894941, rho = -0.160230 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 62 nu = 0.193831 obj = -49.736587, rho = -0.150922 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.171422 obj = -56.542220, rho = -0.131918 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 146 nu = 0.154175 obj = -64.282256, rho = -0.158832 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 71 nu = 0.140735 obj = -72.681160, rho = -0.235091 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 99 nu = 0.130439 obj = -80.664511, rho = -0.351326 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 47 nu = 0.934766 obj = -6.775634, rho = -0.130507 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 47 nu = 0.871698 obj = -7.839383, rho = -0.162386 nSV = 89, nBSV = 85 Total nSV = 89 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 54 nu = 0.795456 obj = -9.000614, rho = -0.133957 nSV = 82, nBSV = 77 Total nSV = 82 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 45 nu = 0.728869 obj = -10.299244, rho = -0.110504 nSV = 74, nBSV = 70 Total nSV = 74 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 41 nu = 0.656297 obj = -11.708742, rho = -0.085120 nSV = 69, nBSV = 62 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 41 nu = 0.583062 obj = -13.326932, rho = -0.092751 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 31 nu = 0.520000 obj = -15.149869, rho = -0.094584 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 46 nu = 0.481883 obj = -17.082756, rho = -0.005594 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 51 nu = 0.424136 obj = -19.120104, rho = -0.002343 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 39 nu = 0.367720 obj = -21.445740, rho = -0.012205 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 68 nu = 0.323571 obj = -24.062974, rho = 0.003758 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 51 nu = 0.285133 obj = -27.145875, rho = 0.044668 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 57 nu = 0.252373 obj = -30.426526, rho = 0.059979 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 47 nu = 0.223681 obj = -34.186309, rho = 0.074472 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 62 nu = 0.200784 obj = -38.040142, rho = 0.134920 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.183179 obj = -41.831967, rho = 0.091787 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 144 nu = 0.156560 obj = -45.130694, rho = 0.058833 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 163 nu = 0.131992 obj = -48.836157, rho = 0.024481 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 150 nu = 0.111540 obj = -52.980115, rho = -0.002057 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 164 nu = 0.098168 obj = -56.836515, rho = -0.087564 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.952090 obj = -7.090762, rho = -0.131745 nSV = 96, nBSV = 93 Total nSV = 96 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -8.282298, rho = -0.077082 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 62 nu = 0.826044 obj = -9.584804, rho = -0.047326 nSV = 86, nBSV = 79 Total nSV = 86 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 43 nu = 0.754833 obj = -11.079273, rho = -0.019365 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 39 nu = 0.697798 obj = -12.741046, rho = -0.010781 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 37 nu = 0.629490 obj = -14.547337, rho = 0.028245 nSV = 66, nBSV = 61 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 74 nu = 0.562793 obj = -16.562924, rho = 0.078749 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 42 nu = 0.511910 obj = -18.876222, rho = 0.115710 nSV = 53, nBSV = 49 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 65 nu = 0.453544 obj = -21.447531, rho = 0.154184 nSV = 47, nBSV = 40 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 48 nu = 0.402823 obj = -24.434015, rho = 0.197393 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 69 nu = 0.360207 obj = -27.785752, rho = 0.192431 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 75 nu = 0.318401 obj = -31.642574, rho = 0.132977 nSV = 38, nBSV = 29 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 38 nu = 0.287423 obj = -36.132686, rho = 0.091244 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 54 nu = 0.264735 obj = -40.890761, rho = 0.220425 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 80 nu = 0.236416 obj = -45.885398, rho = 0.220151 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 82 nu = 0.208865 obj = -51.342856, rho = 0.254070 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.184067 obj = -57.526082, rho = 0.316227 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.158400 obj = -64.292663, rho = 0.338905 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 207 nu = 0.138912 obj = -72.374744, rho = 0.250397 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.122763 obj = -81.551742, rho = 0.160151 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -7.088597, rho = -0.390137 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 92% (92/100) (classification) Accuracy = 90.1% (901/1000) (classification) * optimization finished, #iter = 45 nu = 0.884383 obj = -8.341379, rho = -0.289967 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 96% (96/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 48 nu = 0.842300 obj = -9.682072, rho = -0.228468 nSV = 86, nBSV = 82 Total nSV = 86 Accuracy = 96% (96/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 49 nu = 0.774625 obj = -11.130065, rho = -0.183477 nSV = 79, nBSV = 75 Total nSV = 79 Accuracy = 96% (96/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 55 nu = 0.705439 obj = -12.695908, rho = -0.141101 nSV = 74, nBSV = 68 Total nSV = 74 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 41 nu = 0.627516 obj = -14.458392, rho = -0.175269 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 53 nu = 0.558160 obj = -16.480983, rho = -0.161294 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 38 nu = 0.517656 obj = -18.650850, rho = -0.095100 nSV = 55, nBSV = 47 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.450117 obj = -21.042209, rho = -0.112704 nSV = 50, nBSV = 41 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.401347 obj = -23.879986, rho = -0.156260 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 27 nu = 0.356253 obj = -27.073871, rho = -0.193720 nSV = 37, nBSV = 33 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.321633 obj = -30.500341, rho = -0.161666 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.289424 obj = -33.944518, rho = -0.218767 nSV = 33, nBSV = 23 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *..* optimization finished, #iter = 265 nu = 0.251696 obj = -37.821896, rho = -0.245248 nSV = 31, nBSV = 21 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 53 nu = 0.218945 obj = -42.232641, rho = -0.344927 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 74 nu = 0.200752 obj = -46.782321, rho = -0.414637 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 68 nu = 0.172448 obj = -51.183653, rho = -0.439901 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 270 nu = 0.146837 obj = -56.126919, rho = -0.469041 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 187 nu = 0.129447 obj = -61.414030, rho = -0.383975 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.111611 obj = -66.554921, rho = -0.426080 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 46 nu = 0.840000 obj = -6.296594, rho = 0.162618 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 96% (96/100) (classification) Accuracy = 89.9% (899/1000) (classification) * optimization finished, #iter = 40 nu = 0.800000 obj = -7.335552, rho = 0.059606 nSV = 80, nBSV = 80 Total nSV = 80 Accuracy = 99% (99/100) (classification) Accuracy = 94.4% (944/1000) (classification) * optimization finished, #iter = 45 nu = 0.749992 obj = -8.444394, rho = 0.044880 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 99% (99/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 36 nu = 0.680000 obj = -9.640598, rho = -0.024119 nSV = 68, nBSV = 68 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 43 nu = 0.612317 obj = -10.940296, rho = -0.026722 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 43 nu = 0.545248 obj = -12.409111, rho = -0.030023 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 29 nu = 0.493489 obj = -14.057288, rho = 0.009040 nSV = 50, nBSV = 48 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 29 nu = 0.442258 obj = -15.791379, rho = -0.035047 nSV = 46, nBSV = 43 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 35 nu = 0.394847 obj = -17.605438, rho = -0.084502 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 57 nu = 0.343556 obj = -19.547009, rho = -0.112276 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 54 nu = 0.302030 obj = -21.734128, rho = -0.124335 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.258683 obj = -24.192499, rho = -0.128523 nSV = 31, nBSV = 21 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 73 nu = 0.222611 obj = -27.205643, rho = -0.134340 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 87 nu = 0.195685 obj = -30.778395, rho = -0.143809 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 58 nu = 0.172521 obj = -34.855953, rho = -0.161099 nSV = 20, nBSV = 15 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 51 nu = 0.154019 obj = -39.640945, rho = -0.203283 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.134877 obj = -45.162757, rho = -0.230513 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 49 nu = 0.120436 obj = -51.942539, rho = -0.277427 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 58 nu = 0.107861 obj = -59.562023, rho = -0.313446 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 39 nu = 0.096023 obj = -68.847354, rho = -0.312090 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 51 nu = 0.925643 obj = -7.126138, rho = 0.000462 nSV = 95, nBSV = 92 Total nSV = 95 Accuracy = 94% (94/100) (classification) Accuracy = 92% (920/1000) (classification) * optimization finished, #iter = 47 nu = 0.893107 obj = -8.397106, rho = -0.144726 nSV = 90, nBSV = 86 Total nSV = 90 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 53 nu = 0.830929 obj = -9.798907, rho = -0.144827 nSV = 87, nBSV = 81 Total nSV = 87 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 58 nu = 0.772865 obj = -11.344858, rho = -0.233754 nSV = 80, nBSV = 75 Total nSV = 80 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 38 nu = 0.720000 obj = -13.075034, rho = -0.250090 nSV = 73, nBSV = 71 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 42 nu = 0.649609 obj = -14.900978, rho = -0.198952 nSV = 68, nBSV = 62 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 41 nu = 0.600000 obj = -16.867815, rho = -0.212325 nSV = 61, nBSV = 58 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 55 nu = 0.527773 obj = -18.935255, rho = -0.219271 nSV = 55, nBSV = 48 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 45 nu = 0.466678 obj = -21.301543, rho = -0.178116 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 94 nu = 0.410019 obj = -23.878993, rho = -0.168353 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.365659 obj = -26.660549, rho = -0.122103 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 49 nu = 0.319154 obj = -29.770685, rho = -0.111343 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 60 nu = 0.286327 obj = -33.117637, rho = -0.068283 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 56 nu = 0.246688 obj = -36.653774, rho = -0.089288 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 83 nu = 0.218211 obj = -40.400803, rho = -0.122440 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.188828 obj = -44.408153, rho = -0.115297 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 181 nu = 0.161874 obj = -48.923173, rho = -0.063712 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 98 nu = 0.138623 obj = -54.070350, rho = -0.022392 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 67 nu = 0.120459 obj = -59.764300, rho = -0.013771 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 57 nu = 0.103872 obj = -66.215897, rho = -0.004868 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 46 nu = 0.918881 obj = -6.539856, rho = -0.205878 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -7.540097, rho = -0.203739 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 44 nu = 0.767207 obj = -8.660489, rho = -0.213974 nSV = 78, nBSV = 74 Total nSV = 78 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 58 nu = 0.700769 obj = -9.891367, rho = -0.162300 nSV = 75, nBSV = 68 Total nSV = 75 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 45 nu = 0.636764 obj = -11.220649, rho = -0.106352 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 57 nu = 0.576503 obj = -12.626519, rho = -0.089598 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 58 nu = 0.503436 obj = -14.161994, rho = -0.084422 nSV = 55, nBSV = 48 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 40 nu = 0.443838 obj = -15.923425, rho = -0.140994 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 49 nu = 0.386105 obj = -17.925249, rho = -0.145794 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 42 nu = 0.342297 obj = -20.284717, rho = -0.149044 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 72 nu = 0.301608 obj = -22.907114, rho = -0.173212 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 83 nu = 0.267513 obj = -26.008318, rho = -0.165417 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 75 nu = 0.237968 obj = -29.489160, rho = -0.134803 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 83 nu = 0.215629 obj = -33.399500, rho = -0.108065 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 79 nu = 0.190115 obj = -37.520177, rho = -0.077128 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 43 nu = 0.170421 obj = -42.293340, rho = -0.079631 nSV = 18, nBSV = 14 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 57 nu = 0.153560 obj = -47.170305, rho = -0.114139 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 73 nu = 0.138796 obj = -51.832802, rho = -0.141688 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 96 nu = 0.124361 obj = -55.578130, rho = -0.118276 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.105669 obj = -58.619489, rho = -0.130985 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -5.990129, rho = -0.010025 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 96% (96/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 39 nu = 0.769312 obj = -6.919794, rho = -0.020040 nSV = 78, nBSV = 76 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 39 nu = 0.701576 obj = -7.943118, rho = 0.018953 nSV = 73, nBSV = 69 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 35 nu = 0.643816 obj = -9.092081, rho = 0.039045 nSV = 66, nBSV = 64 Total nSV = 66 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 73 nu = 0.579310 obj = -10.308236, rho = -0.005695 nSV = 61, nBSV = 54 Total nSV = 61 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 43 nu = 0.520407 obj = -11.680981, rho = 0.017902 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 28 nu = 0.456994 obj = -13.234517, rho = 0.027081 nSV = 47, nBSV = 44 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 32 nu = 0.424026 obj = -14.879838, rho = -0.011505 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 54 nu = 0.377192 obj = -16.492221, rho = -0.062299 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 51 nu = 0.328192 obj = -18.247522, rho = -0.062338 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.285501 obj = -20.068189, rho = -0.088944 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 98 nu = 0.244449 obj = -22.136061, rho = -0.103275 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 38 nu = 0.219386 obj = -24.440547, rho = -0.185689 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 46 nu = 0.190737 obj = -26.431523, rho = -0.202343 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 39 nu = 0.160889 obj = -28.610316, rho = -0.161341 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 59 nu = 0.142161 obj = -30.771764, rho = -0.126997 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.119378 obj = -32.541647, rho = -0.094916 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 81 nu = 0.099293 obj = -34.399224, rho = -0.063361 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 75 nu = 0.083159 obj = -36.083071, rho = -0.049827 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 93 nu = 0.068536 obj = -37.801294, rho = -0.071139 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 39 nu = 0.740000 obj = -6.322965, rho = 0.469978 nSV = 74, nBSV = 74 Total nSV = 74 Accuracy = 66% (66/100) (classification) Accuracy = 57.9% (579/1000) (classification) * optimization finished, #iter = 39 nu = 0.740000 obj = -7.680771, rho = 0.324606 nSV = 74, nBSV = 74 Total nSV = 74 Accuracy = 77% (77/100) (classification) Accuracy = 72.6% (726/1000) (classification) * optimization finished, #iter = 38 nu = 0.740000 obj = -9.176184, rho = 0.139363 nSV = 74, nBSV = 74 Total nSV = 74 Accuracy = 92% (92/100) (classification) Accuracy = 87.9% (879/1000) (classification) * optimization finished, #iter = 41 nu = 0.720000 obj = -10.723485, rho = 0.004604 nSV = 74, nBSV = 71 Total nSV = 74 Accuracy = 97% (97/100) (classification) Accuracy = 93.4% (934/1000) (classification) * optimization finished, #iter = 35 nu = 0.666233 obj = -12.409818, rho = -0.009447 nSV = 68, nBSV = 65 Total nSV = 68 Accuracy = 97% (97/100) (classification) Accuracy = 94.6% (946/1000) (classification) * optimization finished, #iter = 50 nu = 0.613477 obj = -14.234895, rho = -0.038192 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 43 nu = 0.562573 obj = -16.184471, rho = -0.103344 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 41 nu = 0.510178 obj = -18.239410, rho = -0.118873 nSV = 53, nBSV = 49 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 55 nu = 0.453659 obj = -20.407456, rho = -0.129571 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 64 nu = 0.395797 obj = -22.762702, rho = -0.062323 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 51 nu = 0.350506 obj = -25.400857, rho = -0.023287 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 60 nu = 0.306405 obj = -28.246542, rho = -0.015248 nSV = 35, nBSV = 26 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 70 nu = 0.265421 obj = -31.504824, rho = 0.033153 nSV = 32, nBSV = 23 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 39 nu = 0.235231 obj = -35.143476, rho = 0.032199 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.207402 obj = -38.963439, rho = -0.003186 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 74 nu = 0.179812 obj = -43.090781, rho = -0.072789 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 82 nu = 0.158125 obj = -47.502669, rho = -0.043432 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 162 nu = 0.135530 obj = -52.228932, rho = -0.014474 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 85 nu = 0.115949 obj = -57.592570, rho = 0.043131 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 69 nu = 0.104857 obj = -63.324693, rho = -0.131062 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -7.259324, rho = -0.456752 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 87% (87/100) (classification) Accuracy = 83% (830/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -8.572105, rho = -0.307752 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 48 nu = 0.854041 obj = -9.944883, rho = -0.237805 nSV = 88, nBSV = 84 Total nSV = 88 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.792110 obj = -11.456319, rho = -0.211068 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.734723 obj = -13.066987, rho = -0.217740 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 50 nu = 0.660042 obj = -14.739703, rho = -0.174038 nSV = 68, nBSV = 64 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 73 nu = 0.590777 obj = -16.543123, rho = -0.170803 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 42 nu = 0.520193 obj = -18.590050, rho = -0.153711 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 55 nu = 0.458567 obj = -20.803481, rho = -0.187257 nSV = 48, nBSV = 44 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 65 nu = 0.401442 obj = -23.311302, rho = -0.198147 nSV = 45, nBSV = 36 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 99 nu = 0.351907 obj = -26.176703, rho = -0.289083 nSV = 40, nBSV = 30 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 91 nu = 0.306547 obj = -29.533043, rho = -0.323149 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 56 nu = 0.270177 obj = -33.377292, rho = -0.393603 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 75 nu = 0.239303 obj = -37.844365, rho = -0.359955 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 43 nu = 0.219842 obj = -42.813487, rho = -0.324195 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 49 nu = 0.194265 obj = -47.871878, rho = -0.252087 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.169593 obj = -53.462649, rho = -0.237487 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.148284 obj = -60.063266, rho = -0.245286 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 81 nu = 0.130785 obj = -67.640663, rho = -0.224470 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 61 nu = 0.113547 obj = -76.212453, rho = -0.218968 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 51 nu = 0.911331 obj = -6.711400, rho = -0.363908 nSV = 93, nBSV = 90 Total nSV = 93 Accuracy = 95% (95/100) (classification) Accuracy = 94.1% (941/1000) (classification) * optimization finished, #iter = 54 nu = 0.846570 obj = -7.819089, rho = -0.327947 nSV = 87, nBSV = 83 Total nSV = 87 Accuracy = 98% (98/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 42 nu = 0.780000 obj = -9.075498, rho = -0.324362 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 98% (98/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 45 nu = 0.731160 obj = -10.426487, rho = -0.310930 nSV = 75, nBSV = 70 Total nSV = 75 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 48 nu = 0.668870 obj = -11.862859, rho = -0.262667 nSV = 68, nBSV = 64 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 43 nu = 0.599287 obj = -13.415912, rho = -0.251705 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 33 nu = 0.540000 obj = -15.121644, rho = -0.211632 nSV = 55, nBSV = 53 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 46 nu = 0.480000 obj = -16.842685, rho = -0.169239 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 83 nu = 0.423882 obj = -18.687284, rho = -0.128898 nSV = 46, nBSV = 37 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 41 nu = 0.368256 obj = -20.750455, rho = -0.078341 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 92 nu = 0.327110 obj = -22.806051, rho = -0.114378 nSV = 37, nBSV = 28 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 65 nu = 0.282525 obj = -25.091541, rho = -0.050540 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 62 nu = 0.245204 obj = -27.517399, rho = -0.039964 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 70 nu = 0.209666 obj = -30.008382, rho = -0.041211 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 62 nu = 0.178022 obj = -32.959477, rho = -0.038395 nSV = 20, nBSV = 15 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 88 nu = 0.151819 obj = -36.302250, rho = -0.046567 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 62 nu = 0.131909 obj = -40.153326, rho = -0.027853 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 137 nu = 0.114517 obj = -44.329539, rho = 0.029359 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 72 nu = 0.101211 obj = -48.757935, rho = -0.014820 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 160 nu = 0.089409 obj = -52.844033, rho = -0.142716 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.893702 obj = -6.329326, rho = -0.103699 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 98% (98/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 52 nu = 0.822419 obj = -7.264211, rho = -0.146958 nSV = 85, nBSV = 81 Total nSV = 85 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 43 nu = 0.755717 obj = -8.290340, rho = -0.164680 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 41 nu = 0.682212 obj = -9.373712, rho = -0.189992 nSV = 71, nBSV = 66 Total nSV = 71 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 63 nu = 0.607412 obj = -10.565210, rho = -0.153734 nSV = 64, nBSV = 57 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 48 nu = 0.549708 obj = -11.828321, rho = -0.107890 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 44 nu = 0.474887 obj = -13.181515, rho = -0.098439 nSV = 51, nBSV = 44 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 72 nu = 0.416844 obj = -14.753134, rho = -0.090000 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 25 nu = 0.364283 obj = -16.534715, rho = -0.071628 nSV = 38, nBSV = 35 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 33 nu = 0.326385 obj = -18.450575, rho = -0.106349 nSV = 35, nBSV = 30 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 65 nu = 0.284523 obj = -20.471099, rho = -0.086645 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 56 nu = 0.251129 obj = -22.701320, rho = -0.094882 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 79 nu = 0.218244 obj = -24.964333, rho = -0.125677 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 65 nu = 0.185048 obj = -27.591457, rho = -0.108533 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 50 nu = 0.158450 obj = -30.803942, rho = -0.130104 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 64 nu = 0.139013 obj = -34.518064, rho = -0.181186 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 57 nu = 0.122898 obj = -38.623529, rho = -0.264687 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 63 nu = 0.109824 obj = -43.112777, rho = -0.165680 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 160 nu = 0.094247 obj = -47.994888, rho = -0.118388 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 150 nu = 0.083795 obj = -53.685246, rho = 0.010303 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.933990 obj = -6.959753, rho = -0.065112 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 96% (96/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -8.138305, rho = -0.029199 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 41 nu = 0.813828 obj = -9.446342, rho = -0.001285 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 41 nu = 0.743122 obj = -10.912860, rho = -0.017788 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 53 nu = 0.674610 obj = -12.564739, rho = -0.009713 nSV = 71, nBSV = 66 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 34 nu = 0.620000 obj = -14.419391, rho = -0.071163 nSV = 63, nBSV = 61 Total nSV = 63 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 38 nu = 0.561388 obj = -16.424451, rho = -0.087030 nSV = 58, nBSV = 54 Total nSV = 58 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 35 nu = 0.515070 obj = -18.635881, rho = -0.115964 nSV = 53, nBSV = 50 Total nSV = 53 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 68 nu = 0.456092 obj = -20.970220, rho = -0.177282 nSV = 50, nBSV = 43 Total nSV = 50 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 47 nu = 0.400000 obj = -23.638316, rho = -0.183533 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 67 nu = 0.347768 obj = -26.795750, rho = -0.165639 nSV = 40, nBSV = 31 Total nSV = 40 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 69 nu = 0.310630 obj = -30.522479, rho = -0.218971 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 65 nu = 0.273839 obj = -34.907099, rho = -0.191470 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 77 nu = 0.242350 obj = -40.091492, rho = -0.180483 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 58 nu = 0.219410 obj = -46.284609, rho = -0.206960 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 55 nu = 0.194113 obj = -53.644605, rho = -0.220158 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 46 nu = 0.176161 obj = -62.569724, rho = -0.214955 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 77 nu = 0.165099 obj = -72.830886, rho = -0.203109 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 90 nu = 0.151878 obj = -84.048566, rho = -0.162976 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 244 nu = 0.137824 obj = -96.538402, rho = -0.168236 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.800000 obj = -6.330052, rho = 0.352331 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 78% (78/100) (classification) Accuracy = 72.2% (722/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -7.482578, rho = 0.174692 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 92% (92/100) (classification) Accuracy = 88.9% (889/1000) (classification) * optimization finished, #iter = 55 nu = 0.774549 obj = -8.633751, rho = 0.021553 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 97% (97/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 40 nu = 0.701199 obj = -9.807090, rho = -0.002268 nSV = 72, nBSV = 69 Total nSV = 72 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 53 nu = 0.630119 obj = -11.094323, rho = -0.052074 nSV = 67, nBSV = 61 Total nSV = 67 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 41 nu = 0.561916 obj = -12.541491, rho = -0.047744 nSV = 58, nBSV = 54 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 44 nu = 0.503589 obj = -14.126444, rho = -0.028399 nSV = 52, nBSV = 49 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 62 nu = 0.446449 obj = -15.836142, rho = -0.018988 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 74 nu = 0.389497 obj = -17.729991, rho = -0.021331 nSV = 44, nBSV = 36 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 42 nu = 0.359157 obj = -19.756730, rho = -0.166537 nSV = 38, nBSV = 33 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 61 nu = 0.307240 obj = -21.753298, rho = -0.179179 nSV = 36, nBSV = 27 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 96 nu = 0.266152 obj = -24.008930, rho = -0.212261 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 84 nu = 0.228007 obj = -26.601666, rho = -0.221698 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 58 nu = 0.197247 obj = -29.605980, rho = -0.246949 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 74 nu = 0.173602 obj = -33.011641, rho = -0.328332 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 58 nu = 0.149523 obj = -36.839062, rho = -0.315158 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 51 nu = 0.133298 obj = -41.163241, rho = -0.301900 nSV = 16, nBSV = 11 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 54 nu = 0.121537 obj = -45.150729, rho = -0.306922 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.107758 obj = -48.315094, rho = -0.432685 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 96 nu = 0.089878 obj = -51.473177, rho = -0.379835 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -7.069220, rho = -0.260877 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 50 nu = 0.892569 obj = -8.266451, rho = -0.236033 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 45 nu = 0.840000 obj = -9.548045, rho = -0.115981 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 48 nu = 0.754935 obj = -10.933394, rho = -0.104396 nSV = 79, nBSV = 73 Total nSV = 79 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 36 nu = 0.683440 obj = -12.559127, rho = -0.082397 nSV = 70, nBSV = 68 Total nSV = 70 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 39 nu = 0.630662 obj = -14.279501, rho = -0.075972 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 55 nu = 0.560902 obj = -16.186294, rho = -0.117925 nSV = 59, nBSV = 52 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 48 nu = 0.497809 obj = -18.368808, rho = -0.180446 nSV = 55, nBSV = 48 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 38 nu = 0.441157 obj = -20.871356, rho = -0.174430 nSV = 47, nBSV = 43 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 44 nu = 0.396369 obj = -23.747623, rho = -0.134074 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 42 nu = 0.351513 obj = -27.006658, rho = -0.111761 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 51 nu = 0.314576 obj = -30.667939, rho = -0.051787 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 53 nu = 0.279580 obj = -34.812590, rho = -0.058273 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 55 nu = 0.252761 obj = -39.374742, rho = -0.084644 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 57 nu = 0.225585 obj = -44.408248, rho = -0.095420 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 62 nu = 0.197488 obj = -50.075939, rho = -0.118217 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.179385 obj = -56.323110, rho = -0.136927 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.156487 obj = -62.818478, rho = -0.184621 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 94 nu = 0.139368 obj = -70.346323, rho = -0.083829 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 164 nu = 0.126309 obj = -77.621812, rho = 0.140984 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.840000 obj = -6.509094, rho = 0.153039 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 88% (88/100) (classification) Accuracy = 87.2% (872/1000) (classification) * optimization finished, #iter = 43 nu = 0.820000 obj = -7.642223, rho = -0.003880 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 98% (98/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 47 nu = 0.753830 obj = -8.879929, rho = -0.038202 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 98% (98/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 37 nu = 0.700709 obj = -10.279697, rho = -0.095882 nSV = 72, nBSV = 70 Total nSV = 72 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 60 nu = 0.643965 obj = -11.792671, rho = -0.166769 nSV = 67, nBSV = 60 Total nSV = 67 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 62 nu = 0.577593 obj = -13.526483, rho = -0.124774 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 39 nu = 0.524160 obj = -15.498900, rho = -0.176390 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 47 nu = 0.476389 obj = -17.687684, rho = -0.156529 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 33 nu = 0.427593 obj = -20.128123, rho = -0.143715 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 31 nu = 0.383377 obj = -22.858654, rho = -0.180624 nSV = 40, nBSV = 36 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 64 nu = 0.334983 obj = -25.979603, rho = -0.185557 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 44 nu = 0.303137 obj = -29.527025, rho = -0.098241 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 34 nu = 0.275264 obj = -33.532770, rho = -0.057240 nSV = 30, nBSV = 26 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 24 nu = 0.246149 obj = -37.641621, rho = -0.159233 nSV = 27, nBSV = 23 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 26 nu = 0.222047 obj = -41.981058, rho = -0.152814 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 48 nu = 0.190033 obj = -46.710633, rho = -0.181345 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 41 nu = 0.166286 obj = -52.158061, rho = -0.211939 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 58 nu = 0.149214 obj = -58.231486, rho = -0.219758 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.131643 obj = -64.039385, rho = -0.355531 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.112312 obj = -70.540195, rho = -0.362971 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -6.639909, rho = 0.297808 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 92% (92/100) (classification) Accuracy = 86.3% (863/1000) (classification) * optimization finished, #iter = 45 nu = 0.846881 obj = -7.780192, rho = 0.167537 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 98% (98/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 44 nu = 0.796404 obj = -8.945281, rho = 0.077812 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 59 nu = 0.733577 obj = -10.157084, rho = 0.068136 nSV = 76, nBSV = 70 Total nSV = 76 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 52 nu = 0.659702 obj = -11.458525, rho = 0.055314 nSV = 69, nBSV = 64 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 55 nu = 0.583765 obj = -12.844539, rho = 0.070917 nSV = 61, nBSV = 54 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 71 nu = 0.515551 obj = -14.361758, rho = 0.045055 nSV = 56, nBSV = 47 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 44 nu = 0.450800 obj = -16.110126, rho = 0.085882 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 42 nu = 0.393707 obj = -18.121780, rho = 0.084052 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.344031 obj = -20.447241, rho = 0.111205 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 35 nu = 0.306436 obj = -23.147475, rho = 0.081002 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 46 nu = 0.267774 obj = -26.289747, rho = 0.082266 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 43 nu = 0.240000 obj = -29.892637, rho = 0.174836 nSV = 26, nBSV = 22 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 43 nu = 0.210886 obj = -34.066025, rho = 0.178074 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 33 nu = 0.192344 obj = -38.945472, rho = 0.281178 nSV = 21, nBSV = 17 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 82 nu = 0.174015 obj = -44.152723, rho = 0.348246 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.151350 obj = -50.119451, rho = 0.380333 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*..* optimization finished, #iter = 342 nu = 0.131820 obj = -57.442142, rho = 0.371254 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 56 nu = 0.120000 obj = -66.307221, rho = 0.402872 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 60 nu = 0.109345 obj = -75.872251, rho = 0.487584 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 45 nu = 0.887045 obj = -6.582700, rho = 0.086749 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 95% (95/100) (classification) Accuracy = 93.4% (934/1000) (classification) * optimization finished, #iter = 43 nu = 0.850053 obj = -7.669128, rho = 0.002549 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 41 nu = 0.780859 obj = -8.812341, rho = 0.020563 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 44 nu = 0.714663 obj = -10.025440, rho = -0.023875 nSV = 73, nBSV = 70 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.644221 obj = -11.358093, rho = -0.096739 nSV = 67, nBSV = 63 Total nSV = 67 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 48 nu = 0.565589 obj = -12.837914, rho = -0.115080 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 37 nu = 0.498915 obj = -14.574414, rho = -0.088151 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 37 nu = 0.448971 obj = -16.563460, rho = -0.067688 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 53 nu = 0.398692 obj = -18.805676, rho = -0.099692 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 35 nu = 0.356996 obj = -21.318950, rho = -0.053916 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 51 nu = 0.315770 obj = -24.188229, rho = -0.044912 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 37 nu = 0.283251 obj = -27.486335, rho = 0.001523 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 29 nu = 0.254765 obj = -31.103776, rho = 0.010768 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 37 nu = 0.230693 obj = -34.947893, rho = -0.154802 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 36 nu = 0.199360 obj = -39.203148, rho = -0.218677 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 71 nu = 0.179620 obj = -43.980241, rho = -0.297735 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 55 nu = 0.155000 obj = -49.164115, rho = -0.292968 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.134754 obj = -55.416419, rho = -0.278962 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.123544 obj = -62.305508, rho = -0.222929 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 167 nu = 0.111103 obj = -68.992519, rho = -0.121393 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -7.382398, rho = -0.239556 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 96% (96/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -8.696978, rho = -0.240043 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 50 nu = 0.868594 obj = -10.134003, rho = -0.189485 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 51 nu = 0.803238 obj = -11.710964, rho = -0.164870 nSV = 82, nBSV = 78 Total nSV = 82 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 43 nu = 0.729486 obj = -13.461832, rho = -0.140438 nSV = 75, nBSV = 71 Total nSV = 75 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 40 nu = 0.675682 obj = -15.382863, rho = -0.117318 nSV = 70, nBSV = 65 Total nSV = 70 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 66 nu = 0.603254 obj = -17.430592, rho = -0.098366 nSV = 65, nBSV = 57 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 50 nu = 0.534083 obj = -19.785050, rho = -0.142847 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 46 nu = 0.478922 obj = -22.477928, rho = -0.176811 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 48 nu = 0.424911 obj = -25.442218, rho = -0.136197 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 68 nu = 0.375651 obj = -28.908469, rho = -0.124308 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 57 nu = 0.339927 obj = -32.858927, rho = -0.146987 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 80 nu = 0.302727 obj = -37.058875, rho = -0.120605 nSV = 35, nBSV = 26 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 54 nu = 0.276430 obj = -41.539874, rho = -0.235969 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 73 nu = 0.238915 obj = -46.301557, rho = -0.225468 nSV = 30, nBSV = 20 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 44 nu = 0.207421 obj = -52.000168, rho = -0.240614 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 57 nu = 0.189184 obj = -58.069275, rho = -0.342581 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 60 nu = 0.166647 obj = -64.297954, rho = -0.354844 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.146946 obj = -70.376025, rho = -0.429502 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 164 nu = 0.122564 obj = -77.333052, rho = -0.433428 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -6.922732, rho = -0.252366 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 96% (96/100) (classification) Accuracy = 93.5% (935/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -8.078857, rho = -0.175162 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 99% (99/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 45 nu = 0.837661 obj = -9.264000, rho = -0.121090 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 44 nu = 0.755929 obj = -10.502617, rho = -0.066037 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 45 nu = 0.674555 obj = -11.868008, rho = -0.034741 nSV = 71, nBSV = 66 Total nSV = 71 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 37 nu = 0.595488 obj = -13.370917, rho = -0.043197 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 47 nu = 0.540000 obj = -15.070433, rho = -0.032019 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 54 nu = 0.479971 obj = -16.852715, rho = 0.022165 nSV = 51, nBSV = 44 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 67 nu = 0.418775 obj = -18.805582, rho = 0.045126 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 78 nu = 0.363669 obj = -21.028406, rho = 0.046448 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 55 nu = 0.316834 obj = -23.600374, rho = 0.032979 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 34 nu = 0.286985 obj = -26.427263, rho = 0.092563 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 32 nu = 0.259013 obj = -29.219561, rho = 0.029014 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 56 nu = 0.224429 obj = -31.824399, rho = -0.024720 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 32 nu = 0.192132 obj = -34.666398, rho = -0.004247 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 74 nu = 0.168418 obj = -37.550026, rho = 0.015632 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.143923 obj = -40.326738, rho = 0.017730 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 73 nu = 0.122452 obj = -43.180856, rho = 0.036616 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 174 nu = 0.104192 obj = -45.597034, rho = 0.026364 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 175 nu = 0.085347 obj = -48.018500, rho = 0.022938 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 48 nu = 0.959043 obj = -7.340596, rho = -0.340270 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 95% (95/100) (classification) Accuracy = 94% (940/1000) (classification) * optimization finished, #iter = 50 nu = 0.901771 obj = -8.659520, rho = -0.279433 nSV = 93, nBSV = 89 Total nSV = 93 Accuracy = 97% (97/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 49 nu = 0.852638 obj = -10.122047, rho = -0.181677 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 47 nu = 0.798698 obj = -11.735171, rho = -0.127802 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 45 nu = 0.729106 obj = -13.524634, rho = -0.150067 nSV = 74, nBSV = 71 Total nSV = 74 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 44 nu = 0.658914 obj = -15.568891, rho = -0.137177 nSV = 68, nBSV = 63 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 41 nu = 0.592579 obj = -17.960378, rho = -0.102848 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.549073 obj = -20.566617, rho = -0.089983 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 36 nu = 0.485841 obj = -23.527337, rho = -0.132195 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 31 nu = 0.451849 obj = -26.843252, rho = -0.088335 nSV = 46, nBSV = 43 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 61 nu = 0.399847 obj = -30.328910, rho = -0.128192 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 61 nu = 0.361157 obj = -34.142667, rho = -0.210475 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.313198 obj = -38.438622, rho = -0.228346 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 51 nu = 0.284771 obj = -43.185823, rho = -0.333133 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 64 nu = 0.253736 obj = -48.129322, rho = -0.386609 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 153 nu = 0.218721 obj = -53.436649, rho = -0.398706 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 165 nu = 0.186846 obj = -59.816826, rho = -0.409624 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.162976 obj = -67.488690, rho = -0.368686 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 118 nu = 0.142716 obj = -76.701775, rho = -0.444547 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 96 nu = 0.128723 obj = -87.352430, rho = -0.487390 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -6.804486, rho = -0.606487 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 71% (71/100) (classification) Accuracy = 70.1% (701/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -8.183051, rho = -0.498557 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 88% (88/100) (classification) Accuracy = 88.8% (888/1000) (classification) * optimization finished, #iter = 41 nu = 0.800000 obj = -9.682166, rho = -0.397119 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 96% (96/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 52 nu = 0.769269 obj = -11.253032, rho = -0.279309 nSV = 79, nBSV = 74 Total nSV = 79 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 48 nu = 0.702711 obj = -12.980472, rho = -0.228130 nSV = 72, nBSV = 68 Total nSV = 72 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 43 nu = 0.643222 obj = -14.871680, rho = -0.210088 nSV = 67, nBSV = 62 Total nSV = 67 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 36 nu = 0.585456 obj = -16.932653, rho = -0.200346 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 33 nu = 0.534155 obj = -19.075893, rho = -0.178260 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 39 nu = 0.480868 obj = -21.314941, rho = -0.129412 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.417595 obj = -23.691415, rho = -0.119057 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.366542 obj = -26.285852, rho = -0.095199 nSV = 42, nBSV = 32 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 71 nu = 0.319825 obj = -29.126924, rho = -0.173117 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 53 nu = 0.285802 obj = -32.097763, rho = -0.285934 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 183 nu = 0.244923 obj = -34.937462, rho = -0.251427 nSV = 28, nBSV = 18 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *..* optimization finished, #iter = 214 nu = 0.205678 obj = -38.300651, rho = -0.268245 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.178087 obj = -42.345724, rho = -0.319860 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 72 nu = 0.154200 obj = -46.769531, rho = -0.205362 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 56 nu = 0.134630 obj = -51.403447, rho = -0.081020 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 81 nu = 0.121344 obj = -55.705607, rho = -0.111688 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 88 nu = 0.101450 obj = -59.694655, rho = -0.117481 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -6.951134, rho = 0.318961 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 77% (77/100) (classification) Accuracy = 77% (770/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -8.281374, rho = 0.132170 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 93% (93/100) (classification) Accuracy = 91.4% (914/1000) (classification) * optimization finished, #iter = 47 nu = 0.830781 obj = -9.638285, rho = 0.008108 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 99% (99/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 41 nu = 0.767923 obj = -11.094475, rho = 0.020876 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 37 nu = 0.700000 obj = -12.710459, rho = 0.032322 nSV = 70, nBSV = 70 Total nSV = 70 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 40 nu = 0.638042 obj = -14.463348, rho = 0.044915 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 38 nu = 0.569934 obj = -16.412959, rho = 0.057608 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 38 nu = 0.506000 obj = -18.602204, rho = 0.020572 nSV = 53, nBSV = 49 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 38 nu = 0.455041 obj = -20.960677, rho = 0.004673 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 26 nu = 0.401640 obj = -23.660599, rho = -0.082003 nSV = 43, nBSV = 39 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 52 nu = 0.370733 obj = -26.418171, rho = -0.103089 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 88 nu = 0.316853 obj = -29.343900, rho = -0.133016 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 78 nu = 0.278488 obj = -32.704313, rho = -0.167620 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.241083 obj = -36.432721, rho = -0.184102 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 81 nu = 0.211494 obj = -40.679198, rho = -0.187778 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 164 nu = 0.184787 obj = -45.394509, rho = -0.196887 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 94 nu = 0.161978 obj = -50.690381, rho = -0.167068 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 113 nu = 0.142432 obj = -56.535944, rho = -0.172941 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 200 nu = 0.123116 obj = -63.107994, rho = -0.173430 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 246 nu = 0.106414 obj = -70.896813, rho = -0.190725 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 48 nu = 0.895353 obj = -6.676861, rho = -0.226812 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 42 nu = 0.836483 obj = -7.819601, rho = -0.181904 nSV = 84, nBSV = 82 Total nSV = 84 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 40 nu = 0.800000 obj = -9.046069, rho = -0.159132 nSV = 80, nBSV = 80 Total nSV = 80 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 39 nu = 0.733012 obj = -10.327740, rho = -0.141466 nSV = 76, nBSV = 72 Total nSV = 76 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 47 nu = 0.672770 obj = -11.653343, rho = -0.132736 nSV = 70, nBSV = 64 Total nSV = 70 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 39 nu = 0.594963 obj = -13.064177, rho = -0.083785 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 37 nu = 0.520102 obj = -14.664802, rho = -0.065937 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 62 nu = 0.460874 obj = -16.416233, rho = -0.029970 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 40 nu = 0.408736 obj = -18.379235, rho = -0.077900 nSV = 43, nBSV = 39 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 36 nu = 0.359037 obj = -20.511126, rho = -0.101264 nSV = 38, nBSV = 33 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 46 nu = 0.312216 obj = -22.907335, rho = -0.093023 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 31 nu = 0.277941 obj = -25.524276, rho = -0.099025 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 45 nu = 0.239506 obj = -28.412377, rho = -0.094970 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 53 nu = 0.211937 obj = -31.655188, rho = -0.087948 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 76 nu = 0.184166 obj = -35.109288, rho = -0.051903 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 92 nu = 0.158984 obj = -39.240076, rho = -0.027899 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 95 nu = 0.139118 obj = -44.036868, rho = 0.019521 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.120848 obj = -49.360089, rho = 0.032933 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 71 nu = 0.104082 obj = -55.966008, rho = -0.001247 nSV = 14, nBSV = 9 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.097392 obj = -63.232984, rho = 0.086392 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -7.432602, rho = -0.025782 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 94% (94/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 50 nu = 0.921344 obj = -8.762940, rho = 0.067156 nSV = 94, nBSV = 91 Total nSV = 94 Accuracy = 95% (95/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 49 nu = 0.856035 obj = -10.269642, rho = 0.129463 nSV = 88, nBSV = 83 Total nSV = 88 Accuracy = 96% (96/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 41 nu = 0.805348 obj = -11.959868, rho = 0.171029 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 96% (96/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 54 nu = 0.728950 obj = -13.828998, rho = 0.121836 nSV = 77, nBSV = 71 Total nSV = 77 Accuracy = 95% (95/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 70 nu = 0.667062 obj = -15.991499, rho = 0.095955 nSV = 70, nBSV = 64 Total nSV = 70 Accuracy = 95% (95/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 39 nu = 0.602010 obj = -18.531462, rho = 0.089422 nSV = 62, nBSV = 59 Total nSV = 62 Accuracy = 95% (95/100) (classification) Accuracy = 99.3% (993/1000) (classification) * optimization finished, #iter = 63 nu = 0.553185 obj = -21.361804, rho = 0.043774 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 96% (96/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 37 nu = 0.503640 obj = -24.662915, rho = 0.033243 nSV = 52, nBSV = 49 Total nSV = 52 Accuracy = 96% (96/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 48 nu = 0.462978 obj = -28.297659, rho = -0.016916 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 96% (96/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 83 nu = 0.414291 obj = -32.386826, rho = -0.012951 nSV = 44, nBSV = 36 Total nSV = 44 Accuracy = 95% (95/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 59 nu = 0.371344 obj = -37.165530, rho = -0.012979 nSV = 41, nBSV = 32 Total nSV = 41 Accuracy = 95% (95/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 68 nu = 0.328356 obj = -42.768008, rho = -0.046986 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 94% (94/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 94 nu = 0.290839 obj = -49.571922, rho = -0.032974 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 94% (94/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 59 nu = 0.261578 obj = -58.013270, rho = -0.039290 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 95% (95/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 158 nu = 0.235807 obj = -68.276711, rho = -0.017790 nSV = 29, nBSV = 19 Total nSV = 29 Accuracy = 95% (95/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 199 nu = 0.214366 obj = -81.034018, rho = -0.031626 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 94% (94/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 96 nu = 0.196858 obj = -96.790554, rho = -0.050184 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 94% (94/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 197 nu = 0.182014 obj = -116.148980, rho = -0.098640 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 94% (94/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.170389 obj = -140.479264, rho = -0.074179 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 96% (96/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -7.562130, rho = 0.243817 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 85% (85/100) (classification) Accuracy = 85.7% (857/1000) (classification) * optimization finished, #iter = 50 nu = 0.948547 obj = -8.925946, rho = 0.075670 nSV = 96, nBSV = 92 Total nSV = 96 Accuracy = 96% (96/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 49 nu = 0.884889 obj = -10.405706, rho = 0.024777 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 97% (97/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 44 nu = 0.821905 obj = -12.056404, rho = 0.000008 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 44 nu = 0.740000 obj = -13.910273, rho = -0.008614 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 46 nu = 0.680588 obj = -16.023295, rho = -0.057405 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 54 nu = 0.634528 obj = -18.267520, rho = 0.065617 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.555158 obj = -20.748368, rho = 0.118730 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 39 nu = 0.508496 obj = -23.490243, rho = 0.109300 nSV = 53, nBSV = 49 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 71 nu = 0.454140 obj = -26.399508, rho = 0.115517 nSV = 49, nBSV = 41 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 51 nu = 0.407053 obj = -29.480744, rho = 0.069715 nSV = 44, nBSV = 36 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 72 nu = 0.363540 obj = -32.662762, rho = -0.006215 nSV = 39, nBSV = 30 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.311280 obj = -36.054884, rho = -0.015106 nSV = 36, nBSV = 26 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 195 nu = 0.265291 obj = -40.010640, rho = -0.037603 nSV = 33, nBSV = 23 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.....* optimization finished, #iter = 510 nu = 0.230047 obj = -44.646444, rho = -0.028215 nSV = 30, nBSV = 20 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 68 nu = 0.209040 obj = -49.760053, rho = -0.122882 nSV = 27, nBSV = 17 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *..* optimization finished, #iter = 295 nu = 0.180139 obj = -54.841550, rho = -0.123324 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 210 nu = 0.153674 obj = -60.745233, rho = -0.099671 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 256 nu = 0.132782 obj = -67.705846, rho = -0.125251 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 122 nu = 0.113616 obj = -76.188552, rho = -0.129460 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -6.690472, rho = 0.436659 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 76% (76/100) (classification) Accuracy = 65.8% (658/1000) (classification) * optimization finished, #iter = 45 nu = 0.820000 obj = -7.997918, rho = 0.282148 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 89% (89/100) (classification) Accuracy = 82.3% (823/1000) (classification) * optimization finished, #iter = 45 nu = 0.800000 obj = -9.369843, rho = 0.141820 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 94% (94/100) (classification) Accuracy = 91.1% (911/1000) (classification) * optimization finished, #iter = 43 nu = 0.745206 obj = -10.821944, rho = 0.086881 nSV = 76, nBSV = 72 Total nSV = 76 Accuracy = 97% (97/100) (classification) Accuracy = 94.2% (942/1000) (classification) * optimization finished, #iter = 36 nu = 0.680000 obj = -12.430781, rho = 0.062141 nSV = 68, nBSV = 68 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 40 nu = 0.615099 obj = -14.190562, rho = 0.041889 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 38 nu = 0.566218 obj = -16.084893, rho = 0.055699 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 53 nu = 0.501858 obj = -18.102761, rho = 0.068678 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 40 nu = 0.440910 obj = -20.382246, rho = 0.062660 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 66 nu = 0.391216 obj = -22.980684, rho = 0.070707 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 78 nu = 0.339404 obj = -26.004322, rho = 0.077339 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 39 nu = 0.301163 obj = -29.660800, rho = 0.137799 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 37 nu = 0.272402 obj = -33.698905, rho = 0.190578 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 63 nu = 0.241411 obj = -38.146755, rho = 0.157098 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.211421 obj = -43.429354, rho = 0.173983 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 47 nu = 0.196773 obj = -49.166158, rho = 0.237376 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 57 nu = 0.178489 obj = -54.778616, rho = 0.216100 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 56 nu = 0.160839 obj = -60.448596, rho = 0.256868 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 191 nu = 0.137066 obj = -65.817719, rho = 0.269605 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 279 nu = 0.116053 obj = -72.241452, rho = 0.266920 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 49 nu = 0.892141 obj = -6.628462, rho = -0.370959 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 95% (95/100) (classification) Accuracy = 92.2% (922/1000) (classification) * optimization finished, #iter = 51 nu = 0.844642 obj = -7.713296, rho = -0.293801 nSV = 86, nBSV = 81 Total nSV = 86 Accuracy = 94% (94/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 47 nu = 0.768674 obj = -8.925230, rho = -0.259496 nSV = 80, nBSV = 76 Total nSV = 80 Accuracy = 96% (96/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 46 nu = 0.708311 obj = -10.288310, rho = -0.217715 nSV = 75, nBSV = 69 Total nSV = 75 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 44 nu = 0.647343 obj = -11.787000, rho = -0.187481 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 38 nu = 0.584154 obj = -13.475913, rho = -0.157362 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 66 nu = 0.531320 obj = -15.278112, rho = -0.096485 nSV = 56, nBSV = 49 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 64 nu = 0.465601 obj = -17.335244, rho = -0.090033 nSV = 51, nBSV = 44 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 40 nu = 0.414701 obj = -19.755723, rho = -0.086629 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 46 nu = 0.372051 obj = -22.544250, rho = -0.130278 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 54 nu = 0.331759 obj = -25.711934, rho = -0.147051 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 45 nu = 0.304487 obj = -29.247572, rho = -0.227155 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.267409 obj = -33.001252, rho = -0.251658 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 51 nu = 0.239240 obj = -37.325641, rho = -0.298381 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.214896 obj = -42.029312, rho = -0.283260 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 95 nu = 0.192540 obj = -47.106776, rho = -0.229873 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.167097 obj = -52.601764, rho = -0.225323 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 92 nu = 0.148135 obj = -58.869990, rho = -0.222661 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.131862 obj = -65.631691, rho = -0.136970 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 266 nu = 0.117100 obj = -72.360738, rho = 0.008878 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -7.071588, rho = -0.385187 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 96% (96/100) (classification) Accuracy = 94.6% (946/1000) (classification) * optimization finished, #iter = 53 nu = 0.888785 obj = -8.249001, rho = -0.349989 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 96% (96/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 54 nu = 0.831030 obj = -9.547450, rho = -0.286934 nSV = 85, nBSV = 81 Total nSV = 85 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 42 nu = 0.755028 obj = -10.996721, rho = -0.315595 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 44 nu = 0.683124 obj = -12.645650, rho = -0.269130 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 38 nu = 0.620899 obj = -14.529997, rho = -0.263516 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 53 nu = 0.565734 obj = -16.604081, rho = -0.240807 nSV = 59, nBSV = 52 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 49 nu = 0.495308 obj = -19.020070, rho = -0.259523 nSV = 54, nBSV = 47 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 41 nu = 0.451062 obj = -21.821058, rho = -0.274043 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 37 nu = 0.401983 obj = -25.101715, rho = -0.291525 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 40 nu = 0.368810 obj = -28.755693, rho = -0.260668 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 32 nu = 0.335376 obj = -32.895421, rho = -0.254126 nSV = 34, nBSV = 31 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 74 nu = 0.301106 obj = -37.269728, rho = -0.219557 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.272313 obj = -42.049022, rho = -0.181624 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 66 nu = 0.238679 obj = -47.462024, rho = -0.128074 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 89 nu = 0.207681 obj = -53.824814, rho = -0.123242 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.185346 obj = -61.264158, rho = -0.063452 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 79 nu = 0.168736 obj = -69.508626, rho = -0.028325 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 53 nu = 0.155634 obj = -78.191474, rho = -0.112130 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 71 nu = 0.137171 obj = -86.442798, rho = -0.146392 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -7.344891, rho = -0.068104 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 91% (91/100) (classification) Accuracy = 94.6% (946/1000) (classification) * optimization finished, #iter = 48 nu = 0.920319 obj = -8.608304, rho = -0.151104 nSV = 94, nBSV = 91 Total nSV = 94 Accuracy = 94% (94/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 48 nu = 0.860000 obj = -9.987778, rho = -0.133231 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 94% (94/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 47 nu = 0.787479 obj = -11.541738, rho = -0.127408 nSV = 81, nBSV = 76 Total nSV = 81 Accuracy = 95% (95/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 37 nu = 0.705360 obj = -13.348040, rho = -0.128641 nSV = 72, nBSV = 70 Total nSV = 72 Accuracy = 95% (95/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 47 nu = 0.638728 obj = -15.437655, rho = -0.097562 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 95% (95/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 62 nu = 0.588578 obj = -17.856887, rho = -0.066633 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 96% (96/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 29 nu = 0.540000 obj = -20.624195, rho = -0.089677 nSV = 54, nBSV = 54 Total nSV = 54 Accuracy = 96% (96/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 64 nu = 0.491624 obj = -23.556354, rho = -0.187926 nSV = 53, nBSV = 46 Total nSV = 53 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 73 nu = 0.434294 obj = -26.978215, rho = -0.176242 nSV = 47, nBSV = 40 Total nSV = 47 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 72 nu = 0.389622 obj = -31.052821, rho = -0.248014 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 61 nu = 0.351859 obj = -35.676553, rho = -0.337185 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 46 nu = 0.316258 obj = -41.057467, rho = -0.350356 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 75 nu = 0.284182 obj = -47.345888, rho = -0.350237 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 68 nu = 0.268161 obj = -54.247829, rho = -0.447845 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 75 nu = 0.242280 obj = -61.343043, rho = -0.567551 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) .*.* optimization finished, #iter = 241 nu = 0.214584 obj = -69.134456, rho = -0.678563 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.189918 obj = -78.038684, rho = -0.706988 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.167738 obj = -88.153503, rho = -0.748705 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 82 nu = 0.149480 obj = -99.839771, rho = -0.806792 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -6.450578, rho = -0.395899 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 93% (930/1000) (classification) * optimization finished, #iter = 48 nu = 0.832944 obj = -7.514654, rho = -0.302766 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 43 nu = 0.757698 obj = -8.655762, rho = -0.278078 nSV = 78, nBSV = 73 Total nSV = 78 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 44 nu = 0.684191 obj = -9.949263, rho = -0.286936 nSV = 72, nBSV = 68 Total nSV = 72 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 39 nu = 0.640000 obj = -11.374146, rho = -0.270589 nSV = 65, nBSV = 63 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 40 nu = 0.582646 obj = -12.821343, rho = -0.258865 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 43 nu = 0.512487 obj = -14.374739, rho = -0.281570 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 35 nu = 0.453978 obj = -16.107054, rho = -0.288267 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 31 nu = 0.404354 obj = -17.953264, rho = -0.369157 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 56 nu = 0.347457 obj = -20.015618, rho = -0.355130 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 58 nu = 0.304978 obj = -22.370947, rho = -0.353856 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 81 nu = 0.267827 obj = -25.037993, rho = -0.411466 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 77 nu = 0.234128 obj = -28.032097, rho = -0.403156 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.209765 obj = -31.325969, rho = -0.413195 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 49 nu = 0.186102 obj = -34.764363, rho = -0.408130 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 89 nu = 0.167330 obj = -37.829189, rho = -0.480299 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 47 nu = 0.149872 obj = -40.492182, rho = -0.637257 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 66 nu = 0.124512 obj = -42.520799, rho = -0.672812 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .**.* optimization finished, #iter = 156 nu = 0.102024 obj = -44.627603, rho = -0.694145 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 181 nu = 0.083572 obj = -47.004058, rho = -0.696473 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -6.878085, rho = -0.099274 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 92% (92/100) (classification) Accuracy = 95.1% (951/1000) (classification) * optimization finished, #iter = 46 nu = 0.869553 obj = -8.012951, rho = -0.156784 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 96% (96/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 44 nu = 0.811849 obj = -9.255420, rho = -0.158047 nSV = 83, nBSV = 79 Total nSV = 83 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 43 nu = 0.740000 obj = -10.611742, rho = -0.153081 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.661324 obj = -12.160390, rho = -0.147508 nSV = 69, nBSV = 63 Total nSV = 69 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 42 nu = 0.596320 obj = -13.928522, rho = -0.165859 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 34 nu = 0.545082 obj = -15.958768, rho = -0.217980 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 33 nu = 0.483048 obj = -18.233336, rho = -0.207373 nSV = 50, nBSV = 47 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 74 nu = 0.436361 obj = -20.789791, rho = -0.184656 nSV = 47, nBSV = 39 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 92 nu = 0.386085 obj = -23.755481, rho = -0.183809 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 78 nu = 0.347568 obj = -27.182845, rho = -0.174081 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 71 nu = 0.316361 obj = -30.960524, rho = -0.095654 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 75 nu = 0.279444 obj = -35.302054, rho = -0.096230 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 47 nu = 0.248758 obj = -40.361424, rho = -0.071323 nSV = 27, nBSV = 23 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 57 nu = 0.226768 obj = -46.075303, rho = -0.075446 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 85 nu = 0.207398 obj = -52.218028, rho = -0.012569 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.184053 obj = -58.620408, rho = 0.027528 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 63 nu = 0.163139 obj = -65.908233, rho = 0.022242 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 85 nu = 0.141375 obj = -74.184766, rho = 0.048195 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.127870 obj = -83.714440, rho = 0.057044 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -6.749159, rho = -0.015827 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 94% (94/100) (classification) Accuracy = 91.3% (913/1000) (classification) * optimization finished, #iter = 52 nu = 0.836816 obj = -7.921362, rho = -0.142754 nSV = 86, nBSV = 82 Total nSV = 86 Accuracy = 95% (95/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 42 nu = 0.783879 obj = -9.262423, rho = -0.118696 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 62 nu = 0.741030 obj = -10.691083, rho = -0.089663 nSV = 77, nBSV = 71 Total nSV = 77 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 48 nu = 0.665517 obj = -12.270107, rho = -0.125469 nSV = 70, nBSV = 64 Total nSV = 70 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 38 nu = 0.611958 obj = -14.014171, rho = -0.110916 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 37 nu = 0.548566 obj = -15.926338, rho = -0.148383 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 41 nu = 0.494530 obj = -18.080725, rho = -0.104294 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 59 nu = 0.446171 obj = -20.371540, rho = -0.077691 nSV = 47, nBSV = 40 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 48 nu = 0.387115 obj = -22.991245, rho = -0.091489 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 86 nu = 0.348173 obj = -25.895490, rho = -0.090131 nSV = 38, nBSV = 30 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.305761 obj = -29.114674, rho = -0.051881 nSV = 37, nBSV = 27 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 64 nu = 0.269986 obj = -32.855113, rho = -0.037187 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 65 nu = 0.239368 obj = -37.100600, rho = 0.023320 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 98 nu = 0.217326 obj = -41.569409, rho = 0.028093 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.187794 obj = -46.350323, rho = 0.039111 nSV = 26, nBSV = 15 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.165449 obj = -51.722609, rho = 0.025316 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 146 nu = 0.144198 obj = -57.776438, rho = 0.057194 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 129 nu = 0.124353 obj = -64.884589, rho = 0.066953 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 118 nu = 0.114764 obj = -72.831302, rho = 0.069698 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 48 nu = 0.820000 obj = -6.583110, rho = 0.250596 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 81% (81/100) (classification) Accuracy = 76.8% (768/1000) (classification) * optimization finished, #iter = 42 nu = 0.800000 obj = -7.837877, rho = 0.133563 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 89% (89/100) (classification) Accuracy = 86.5% (865/1000) (classification) * optimization finished, #iter = 50 nu = 0.778890 obj = -9.185397, rho = 0.061074 nSV = 80, nBSV = 76 Total nSV = 80 Accuracy = 94% (94/100) (classification) Accuracy = 93.3% (933/1000) (classification) * optimization finished, #iter = 46 nu = 0.722526 obj = -10.657242, rho = 0.006527 nSV = 75, nBSV = 70 Total nSV = 75 Accuracy = 97% (97/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 46 nu = 0.666361 obj = -12.257146, rho = -0.017940 nSV = 68, nBSV = 66 Total nSV = 68 Accuracy = 97% (97/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 51 nu = 0.605820 obj = -14.052397, rho = 0.029013 nSV = 63, nBSV = 57 Total nSV = 63 Accuracy = 97% (97/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 62 nu = 0.537350 obj = -16.111851, rho = 0.053778 nSV = 58, nBSV = 51 Total nSV = 58 Accuracy = 97% (97/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 35 nu = 0.495736 obj = -18.396067, rho = 0.099132 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 97% (97/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 38 nu = 0.444684 obj = -20.856551, rho = 0.116700 nSV = 47, nBSV = 43 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 30 nu = 0.403393 obj = -23.587627, rho = 0.107124 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 43 nu = 0.354146 obj = -26.534670, rho = 0.076551 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.311513 obj = -29.892820, rho = 0.065484 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 53 nu = 0.275496 obj = -33.776742, rho = 0.059001 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 51 nu = 0.247070 obj = -38.080615, rho = 0.017434 nSV = 27, nBSV = 22 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 74 nu = 0.222831 obj = -42.712211, rho = 0.147215 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 74 nu = 0.192442 obj = -47.842029, rho = 0.154324 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 69 nu = 0.175625 obj = -53.456333, rho = 0.178637 nSV = 19, nBSV = 14 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.154574 obj = -58.714288, rho = 0.139222 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.134245 obj = -64.408527, rho = 0.156791 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 120 nu = 0.118929 obj = -69.627868, rho = 0.337075 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 47 nu = 0.925995 obj = -6.962074, rho = -0.236124 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 95% (95/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 47 nu = 0.863782 obj = -8.166957, rho = -0.226681 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 94% (94/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 41 nu = 0.812693 obj = -9.535017, rho = -0.186341 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 95% (95/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 53 nu = 0.750821 obj = -11.030385, rho = -0.112363 nSV = 78, nBSV = 71 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 57 nu = 0.680801 obj = -12.724120, rho = -0.131804 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 49 nu = 0.621079 obj = -14.659495, rho = -0.102859 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 38 nu = 0.558994 obj = -16.894504, rho = -0.116273 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 28 nu = 0.515564 obj = -19.433594, rho = -0.107166 nSV = 52, nBSV = 49 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 52 nu = 0.459625 obj = -22.232159, rho = -0.122980 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.410789 obj = -25.470837, rho = -0.123477 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 38 nu = 0.370875 obj = -29.189332, rho = -0.071451 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 38 nu = 0.333383 obj = -33.466365, rho = -0.071837 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 46 nu = 0.302642 obj = -38.282191, rho = -0.086291 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 51 nu = 0.272811 obj = -43.642853, rho = -0.150932 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 72 nu = 0.247804 obj = -49.463248, rho = -0.192338 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.217869 obj = -55.986507, rho = -0.152944 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 92 nu = 0.197014 obj = -63.300669, rho = -0.207911 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 116 nu = 0.174920 obj = -71.264517, rho = -0.217190 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 192 nu = 0.153949 obj = -80.032497, rho = -0.229918 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 298 nu = 0.132746 obj = -90.549359, rho = -0.231387 nSV = 20, nBSV = 9 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 46 nu = 0.902805 obj = -6.670851, rho = -0.247222 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 98% (98/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 43 nu = 0.847806 obj = -7.741433, rho = -0.188194 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 43 nu = 0.798395 obj = -8.903430, rho = -0.114157 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 40 nu = 0.730261 obj = -10.109658, rho = -0.087337 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 46 nu = 0.641668 obj = -11.445925, rho = -0.035705 nSV = 67, nBSV = 63 Total nSV = 67 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 38 nu = 0.575538 obj = -12.946292, rho = 0.021193 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 32 nu = 0.522095 obj = -14.545660, rho = 0.023019 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 52 nu = 0.462582 obj = -16.211656, rho = 0.024508 nSV = 48, nBSV = 44 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 50 nu = 0.405285 obj = -18.033537, rho = 0.007086 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.350660 obj = -20.104526, rho = -0.017772 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 45 nu = 0.312757 obj = -22.404319, rho = -0.076286 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 51 nu = 0.267363 obj = -24.946447, rho = -0.026307 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 97 nu = 0.234359 obj = -27.889298, rho = -0.068590 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 31 nu = 0.209404 obj = -31.160639, rho = -0.059709 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 57 nu = 0.181750 obj = -34.552186, rho = -0.082209 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 193 nu = 0.156377 obj = -38.559461, rho = -0.050347 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 64 nu = 0.135465 obj = -43.278593, rho = -0.071650 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 67 nu = 0.121035 obj = -48.586210, rho = -0.070757 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.107045 obj = -54.327497, rho = -0.037715 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 79 nu = 0.094051 obj = -60.612579, rho = -0.042644 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 52 nu = 0.956615 obj = -7.116738, rho = -0.059543 nSV = 97, nBSV = 94 Total nSV = 97 Accuracy = 96% (96/100) (classification) Accuracy = 94.7% (947/1000) (classification) * optimization finished, #iter = 48 nu = 0.885707 obj = -8.334168, rho = -0.014544 nSV = 91, nBSV = 87 Total nSV = 91 Accuracy = 96% (96/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 47 nu = 0.840000 obj = -9.713907, rho = -0.104719 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 40 nu = 0.780000 obj = -11.157839, rho = -0.101703 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 51 nu = 0.712484 obj = -12.708986, rho = -0.083862 nSV = 72, nBSV = 68 Total nSV = 72 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 0.636304 obj = -14.375774, rho = -0.072395 nSV = 66, nBSV = 60 Total nSV = 66 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 36 nu = 0.571127 obj = -16.247529, rho = -0.030458 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 91 nu = 0.509059 obj = -18.219989, rho = -0.019006 nSV = 54, nBSV = 47 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.442682 obj = -20.474043, rho = 0.004031 nSV = 48, nBSV = 39 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 75 nu = 0.385681 obj = -23.203797, rho = 0.004293 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.346068 obj = -26.346701, rho = 0.040546 nSV = 40, nBSV = 30 Total nSV = 40 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.306099 obj = -29.985183, rho = 0.045721 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.275529 obj = -33.954757, rho = 0.001828 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.239815 obj = -38.650375, rho = 0.004189 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *..*...* optimization finished, #iter = 414 nu = 0.216529 obj = -44.032823, rho = -0.040699 nSV = 27, nBSV = 17 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 163 nu = 0.192498 obj = -50.163997, rho = -0.019383 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 185 nu = 0.171156 obj = -57.371456, rho = -0.005771 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 199 nu = 0.153088 obj = -65.850308, rho = 0.068150 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 70 nu = 0.139463 obj = -75.610098, rho = 0.076844 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 53 nu = 0.128811 obj = -85.793661, rho = -0.035415 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 50 nu = 0.840000 obj = -6.466729, rho = -0.452042 nSV = 87, nBSV = 82 Total nSV = 87 Accuracy = 94% (94/100) (classification) Accuracy = 85% (850/1000) (classification) * optimization finished, #iter = 43 nu = 0.820000 obj = -7.574619, rho = -0.334349 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 97% (97/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 46 nu = 0.773037 obj = -8.724673, rho = -0.277900 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 98% (98/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 41 nu = 0.708980 obj = -9.944979, rho = -0.228086 nSV = 73, nBSV = 70 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 39 nu = 0.641069 obj = -11.231667, rho = -0.156755 nSV = 67, nBSV = 63 Total nSV = 67 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 40 nu = 0.574272 obj = -12.631102, rho = -0.181114 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 49 nu = 0.507823 obj = -14.148164, rho = -0.254972 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 41 nu = 0.441053 obj = -15.881947, rho = -0.223506 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 97 nu = 0.383343 obj = -17.881690, rho = -0.223404 nSV = 43, nBSV = 34 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 47 nu = 0.343358 obj = -20.217642, rho = -0.213689 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 97 nu = 0.303631 obj = -22.708025, rho = -0.197197 nSV = 37, nBSV = 26 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 90 nu = 0.271794 obj = -25.569085, rho = -0.193089 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 37 nu = 0.240000 obj = -28.506742, rho = -0.246836 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 74 nu = 0.214762 obj = -31.626436, rho = -0.330473 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 59 nu = 0.185412 obj = -35.009454, rho = -0.282175 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.160074 obj = -38.780621, rho = -0.268903 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.138340 obj = -43.242112, rho = -0.261017 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.123051 obj = -48.083290, rho = -0.187605 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 173 nu = 0.104920 obj = -53.491592, rho = -0.205068 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 165 nu = 0.091980 obj = -59.958613, rho = -0.280168 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 51 nu = 0.913846 obj = -6.921486, rho = 0.024342 nSV = 93, nBSV = 89 Total nSV = 93 Accuracy = 94% (94/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 50 nu = 0.868895 obj = -8.116949, rho = -0.071163 nSV = 90, nBSV = 86 Total nSV = 90 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 41 nu = 0.817224 obj = -9.432741, rho = -0.108164 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 52 nu = 0.746249 obj = -10.866977, rho = -0.076509 nSV = 78, nBSV = 72 Total nSV = 78 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 40 nu = 0.684422 obj = -12.448895, rho = -0.015437 nSV = 71, nBSV = 66 Total nSV = 71 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 45 nu = 0.613496 obj = -14.201916, rho = -0.008702 nSV = 65, nBSV = 59 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 40 nu = 0.555749 obj = -16.188659, rho = -0.052706 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 37 nu = 0.508072 obj = -18.333624, rho = -0.046519 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 46 nu = 0.450871 obj = -20.628619, rho = -0.026014 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 52 nu = 0.401312 obj = -23.132788, rho = 0.000775 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.345417 obj = -25.990844, rho = 0.000324 nSV = 40, nBSV = 31 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 37 nu = 0.305636 obj = -29.432025, rho = 0.040725 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 34 nu = 0.273912 obj = -33.270185, rho = 0.121026 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 88 nu = 0.241937 obj = -37.432251, rho = 0.122656 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 42 nu = 0.218561 obj = -42.015492, rho = 0.076032 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 75 nu = 0.199963 obj = -46.719896, rho = 0.139511 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 55 nu = 0.171396 obj = -51.184396, rho = 0.144023 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 94 nu = 0.151807 obj = -55.921088, rho = 0.020080 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.128128 obj = -60.514929, rho = -0.048383 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 162 nu = 0.109945 obj = -65.442157, rho = -0.131472 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 41 nu = 0.740000 obj = -6.338286, rho = 0.560216 nSV = 75, nBSV = 73 Total nSV = 75 Accuracy = 63% (63/100) (classification) Accuracy = 54.2% (542/1000) (classification) * optimization finished, #iter = 40 nu = 0.740000 obj = -7.705648, rho = 0.439594 nSV = 75, nBSV = 73 Total nSV = 75 Accuracy = 76% (76/100) (classification) Accuracy = 67.7% (677/1000) (classification) * optimization finished, #iter = 40 nu = 0.740000 obj = -9.216578, rho = 0.285889 nSV = 75, nBSV = 73 Total nSV = 75 Accuracy = 92% (92/100) (classification) Accuracy = 85.2% (852/1000) (classification) * optimization finished, #iter = 41 nu = 0.733762 obj = -10.767696, rho = 0.120694 nSV = 74, nBSV = 72 Total nSV = 74 Accuracy = 96% (96/100) (classification) Accuracy = 94.6% (946/1000) (classification) * optimization finished, #iter = 41 nu = 0.673671 obj = -12.382681, rho = 0.112516 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 98% (98/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 41 nu = 0.617277 obj = -14.168571, rho = 0.138931 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 97% (97/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 47 nu = 0.547786 obj = -16.151599, rho = 0.121619 nSV = 58, nBSV = 51 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 37 nu = 0.498105 obj = -18.433194, rho = 0.090593 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 52 nu = 0.445940 obj = -20.902827, rho = 0.022108 nSV = 49, nBSV = 42 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 54 nu = 0.399768 obj = -23.640218, rho = -0.033300 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 48 nu = 0.353018 obj = -26.730516, rho = 0.023906 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 44 nu = 0.312680 obj = -30.229713, rho = 0.049508 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 40 nu = 0.280604 obj = -34.225222, rho = 0.043268 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 62 nu = 0.249733 obj = -38.521779, rho = 0.056001 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 35 nu = 0.214550 obj = -43.615186, rho = 0.045487 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.195346 obj = -49.467191, rho = 0.139964 nSV = 22, nBSV = 17 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 59 nu = 0.177266 obj = -55.610334, rho = 0.114611 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.155341 obj = -62.289544, rho = 0.172830 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 87 nu = 0.133655 obj = -69.919286, rho = 0.218094 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.120083 obj = -79.088967, rho = 0.147031 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -7.027949, rho = -0.492572 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 77% (77/100) (classification) Accuracy = 70% (700/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -8.406106, rho = -0.353397 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 89% (89/100) (classification) Accuracy = 89.2% (892/1000) (classification) * optimization finished, #iter = 42 nu = 0.840000 obj = -9.847411, rho = -0.291176 nSV = 84, nBSV = 84 Total nSV = 84 Accuracy = 94% (94/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 50 nu = 0.796634 obj = -11.350836, rho = -0.206119 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 41 nu = 0.717448 obj = -12.969887, rho = -0.239384 nSV = 73, nBSV = 69 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 38 nu = 0.650696 obj = -14.783359, rho = -0.191314 nSV = 66, nBSV = 64 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 43 nu = 0.579317 obj = -16.754576, rho = -0.180153 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 58 nu = 0.515079 obj = -18.974038, rho = -0.236758 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 51 nu = 0.456107 obj = -21.554086, rho = -0.176871 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 59 nu = 0.405700 obj = -24.476077, rho = -0.229862 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 48 nu = 0.362093 obj = -27.820130, rho = -0.206257 nSV = 41, nBSV = 33 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.321563 obj = -31.655178, rho = -0.237870 nSV = 36, nBSV = 26 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 52 nu = 0.281506 obj = -36.307403, rho = -0.227667 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 60 nu = 0.260311 obj = -41.366304, rho = -0.130711 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 53 nu = 0.237585 obj = -46.962230, rho = -0.103909 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.206251 obj = -53.158141, rho = -0.139699 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 190 nu = 0.181453 obj = -60.428497, rho = -0.196824 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) *.* optimization finished, #iter = 169 nu = 0.160012 obj = -69.177307, rho = -0.217290 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 59 nu = 0.143611 obj = -79.859762, rho = -0.238432 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 89 nu = 0.131748 obj = -91.999678, rho = -0.265349 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 44 nu = 0.876900 obj = -6.488007, rho = -0.052503 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 97% (97/100) (classification) Accuracy = 92.8% (928/1000) (classification) * optimization finished, #iter = 52 nu = 0.807255 obj = -7.588650, rho = -0.017612 nSV = 84, nBSV = 79 Total nSV = 84 Accuracy = 97% (97/100) (classification) Accuracy = 94.5% (945/1000) (classification) * optimization finished, #iter = 44 nu = 0.757493 obj = -8.858528, rho = -0.108955 nSV = 77, nBSV = 74 Total nSV = 77 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 41 nu = 0.709745 obj = -10.231264, rho = -0.133008 nSV = 73, nBSV = 68 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 39 nu = 0.642672 obj = -11.713563, rho = -0.105469 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 36 nu = 0.590566 obj = -13.321678, rho = -0.048572 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 32 nu = 0.522209 obj = -15.065816, rho = -0.048154 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 39 nu = 0.461090 obj = -17.079288, rho = -0.004714 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 31 nu = 0.419015 obj = -19.398739, rho = -0.012589 nSV = 43, nBSV = 39 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 33 nu = 0.371239 obj = -21.852697, rho = 0.001489 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 40 nu = 0.325840 obj = -24.667294, rho = 0.022167 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 43 nu = 0.292729 obj = -27.778546, rho = 0.061004 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 67 nu = 0.256182 obj = -31.277237, rho = 0.064879 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 70 nu = 0.228242 obj = -35.308980, rho = 0.091693 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 36 nu = 0.209243 obj = -39.608721, rho = 0.073899 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 60 nu = 0.187474 obj = -43.558466, rho = 0.078253 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 86 nu = 0.160414 obj = -47.546685, rho = 0.093262 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.137731 obj = -51.970418, rho = 0.134438 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.120102 obj = -56.494488, rho = 0.184436 nSV = 19, nBSV = 8 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 97 nu = 0.103150 obj = -61.243226, rho = 0.243278 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 57 nu = 0.920000 obj = -7.155980, rho = 0.206782 nSV = 95, nBSV = 90 Total nSV = 95 Accuracy = 91% (91/100) (classification) Accuracy = 92.2% (922/1000) (classification) * optimization finished, #iter = 48 nu = 0.895128 obj = -8.420038, rho = 0.059797 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.843805 obj = -9.805854, rho = 0.072653 nSV = 86, nBSV = 82 Total nSV = 86 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.780000 obj = -11.309425, rho = 0.046541 nSV = 78, nBSV = 78 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 44 nu = 0.705441 obj = -12.956285, rho = 0.017394 nSV = 73, nBSV = 68 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 34 nu = 0.640000 obj = -14.833120, rho = -0.009141 nSV = 65, nBSV = 63 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 34 nu = 0.572166 obj = -16.914049, rho = -0.034098 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 38 nu = 0.520930 obj = -19.231032, rho = 0.013797 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 40 nu = 0.459469 obj = -21.828583, rho = 0.032153 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 31 nu = 0.414984 obj = -24.856382, rho = -0.003050 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 38 nu = 0.371788 obj = -28.185385, rho = 0.008742 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 53 nu = 0.327169 obj = -32.014892, rho = 0.046998 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 84 nu = 0.290814 obj = -36.539428, rho = 0.101674 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 50 nu = 0.262872 obj = -41.587230, rho = 0.158239 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 81 nu = 0.237830 obj = -47.021063, rho = 0.226683 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.209769 obj = -53.125618, rho = 0.281413 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 95 nu = 0.182505 obj = -60.330375, rho = 0.340714 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.162330 obj = -68.726014, rho = 0.400326 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 92 nu = 0.146914 obj = -78.402691, rho = 0.468185 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 169 nu = 0.130377 obj = -89.167377, rho = 0.509713 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 49 nu = 0.914230 obj = -6.851446, rho = -0.192483 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 47 nu = 0.851392 obj = -8.039114, rho = -0.151136 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 43 nu = 0.802340 obj = -9.357927, rho = -0.096793 nSV = 82, nBSV = 79 Total nSV = 82 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 39 nu = 0.740195 obj = -10.806171, rho = -0.041906 nSV = 76, nBSV = 74 Total nSV = 76 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 39 nu = 0.669040 obj = -12.442796, rho = -0.011712 nSV = 69, nBSV = 66 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 33 nu = 0.619904 obj = -14.311935, rho = -0.001770 nSV = 62, nBSV = 60 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 37 nu = 0.559306 obj = -16.270385, rho = -0.031520 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 34 nu = 0.497848 obj = -18.525699, rho = -0.051721 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 30 nu = 0.445190 obj = -21.075481, rho = -0.099791 nSV = 48, nBSV = 44 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 84 nu = 0.403362 obj = -23.819102, rho = -0.084809 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 57 nu = 0.352548 obj = -26.966165, rho = -0.070150 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.315971 obj = -30.544016, rho = -0.055394 nSV = 33, nBSV = 29 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 88 nu = 0.286809 obj = -34.306582, rho = -0.016843 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 84 nu = 0.255386 obj = -38.352314, rho = 0.007970 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 99 nu = 0.221079 obj = -42.820808, rho = -0.006014 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.195535 obj = -47.824069, rho = 0.012928 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 296 nu = 0.171902 obj = -53.136091, rho = -0.016225 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..* optimization finished, #iter = 290 nu = 0.145787 obj = -59.456657, rho = -0.022496 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.125625 obj = -67.372431, rho = -0.044049 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 226 nu = 0.109551 obj = -77.130007, rho = -0.040664 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -7.450771, rho = 0.227209 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 84% (84/100) (classification) Accuracy = 83.6% (836/1000) (classification) * optimization finished, #iter = 47 nu = 0.902663 obj = -8.889389, rho = 0.079204 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 88% (88/100) (classification) Accuracy = 94.6% (946/1000) (classification) * optimization finished, #iter = 51 nu = 0.874808 obj = -10.455265, rho = 0.001731 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 96% (96/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 47 nu = 0.816627 obj = -12.165076, rho = -0.054581 nSV = 84, nBSV = 79 Total nSV = 84 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 51 nu = 0.753145 obj = -14.056638, rho = -0.030463 nSV = 78, nBSV = 73 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 44 nu = 0.687778 obj = -16.175282, rho = -0.048677 nSV = 71, nBSV = 65 Total nSV = 71 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 42 nu = 0.612634 obj = -18.633536, rho = -0.051433 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 41 nu = 0.549688 obj = -21.564932, rho = -0.024293 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 80 nu = 0.499441 obj = -24.976204, rho = -0.042075 nSV = 53, nBSV = 45 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 55 nu = 0.447786 obj = -29.050493, rho = -0.030503 nSV = 50, nBSV = 42 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 55 nu = 0.406776 obj = -33.948074, rho = -0.013901 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 41 nu = 0.376297 obj = -39.671353, rho = 0.006403 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 38 nu = 0.352165 obj = -46.079473, rho = 0.121759 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 67 nu = 0.322264 obj = -53.294687, rho = 0.137554 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 58 nu = 0.290196 obj = -61.585576, rho = 0.114643 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 71 nu = 0.266079 obj = -70.938411, rho = 0.127465 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.238573 obj = -81.767825, rho = 0.151945 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 164 nu = 0.211704 obj = -94.833713, rho = 0.156463 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 218 nu = 0.190023 obj = -110.682844, rho = 0.141061 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.170614 obj = -130.349127, rho = 0.159735 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 51 nu = 0.892360 obj = -6.603390, rho = -0.144031 nSV = 91, nBSV = 87 Total nSV = 91 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 45 nu = 0.839750 obj = -7.703182, rho = -0.186787 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 44 nu = 0.777025 obj = -8.906299, rho = -0.176109 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 47 nu = 0.713361 obj = -10.228375, rho = -0.128668 nSV = 73, nBSV = 69 Total nSV = 73 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 48 nu = 0.649785 obj = -11.671412, rho = -0.139404 nSV = 68, nBSV = 62 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 35 nu = 0.584646 obj = -13.257559, rho = -0.186168 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 34 nu = 0.518367 obj = -15.053215, rho = -0.231444 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 43 nu = 0.465996 obj = -17.077650, rho = -0.167517 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 50 nu = 0.414551 obj = -19.313469, rho = -0.096055 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 30 nu = 0.365462 obj = -21.896123, rho = -0.079849 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 34 nu = 0.334503 obj = -24.737175, rho = 0.016753 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 68 nu = 0.297793 obj = -27.608408, rho = -0.010741 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.256431 obj = -30.908083, rho = 0.004316 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 68 nu = 0.223273 obj = -34.777358, rho = 0.023171 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 51 nu = 0.198507 obj = -39.247791, rho = 0.139042 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 39 nu = 0.179826 obj = -44.117138, rho = 0.160215 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.155335 obj = -49.472961, rho = 0.122924 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 36 nu = 0.136939 obj = -55.734007, rho = 0.117622 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 38 nu = 0.125177 obj = -62.207151, rho = 0.066806 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 87 nu = 0.112557 obj = -68.346759, rho = 0.026199 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 46 nu = 0.864038 obj = -6.386088, rho = -0.146806 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 93% (93/100) (classification) Accuracy = 93.2% (932/1000) (classification) * optimization finished, #iter = 44 nu = 0.802620 obj = -7.433826, rho = -0.204422 nSV = 83, nBSV = 79 Total nSV = 83 Accuracy = 96% (96/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 45 nu = 0.740000 obj = -8.632009, rho = -0.225353 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 96% (96/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 41 nu = 0.677699 obj = -9.985098, rho = -0.228809 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 43 nu = 0.629347 obj = -11.470124, rho = -0.201651 nSV = 66, nBSV = 60 Total nSV = 66 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.569573 obj = -13.112758, rho = -0.171647 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 42 nu = 0.510722 obj = -14.923415, rho = -0.233126 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 33 nu = 0.453943 obj = -17.018143, rho = -0.259051 nSV = 47, nBSV = 44 Total nSV = 47 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.408114 obj = -19.393089, rho = -0.289073 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 32 nu = 0.374477 obj = -21.952939, rho = -0.216067 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 65 nu = 0.331153 obj = -24.693200, rho = -0.204029 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 51 nu = 0.293300 obj = -27.731068, rho = -0.253247 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 60 nu = 0.259336 obj = -31.058649, rho = -0.248606 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 97% (97/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 62 nu = 0.227542 obj = -34.846786, rho = -0.299992 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 76 nu = 0.195489 obj = -39.301918, rho = -0.330306 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.176946 obj = -44.413233, rho = -0.271398 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 80 nu = 0.153956 obj = -50.185801, rho = -0.212198 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.139361 obj = -56.789211, rho = -0.292099 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.120827 obj = -64.102811, rho = -0.339550 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 181 nu = 0.104078 obj = -73.237725, rho = -0.340297 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.955280 obj = -7.231750, rho = -0.113490 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 46 nu = 0.901434 obj = -8.487596, rho = -0.042949 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 98% (98/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 47 nu = 0.858069 obj = -9.849646, rho = -0.053770 nSV = 87, nBSV = 83 Total nSV = 87 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 45 nu = 0.783297 obj = -11.309806, rho = -0.084276 nSV = 81, nBSV = 77 Total nSV = 81 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 49 nu = 0.719486 obj = -12.909978, rho = -0.080892 nSV = 74, nBSV = 70 Total nSV = 74 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 45 nu = 0.648109 obj = -14.658424, rho = -0.047170 nSV = 66, nBSV = 61 Total nSV = 66 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 48 nu = 0.568966 obj = -16.651845, rho = -0.037767 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 37 nu = 0.517939 obj = -18.916253, rho = -0.115051 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 43 nu = 0.472033 obj = -21.180591, rho = -0.106078 nSV = 51, nBSV = 44 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.410741 obj = -23.617590, rho = -0.097008 nSV = 46, nBSV = 38 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 58 nu = 0.358973 obj = -26.409249, rho = -0.083973 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 42 nu = 0.314289 obj = -29.538023, rho = -0.081071 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 95 nu = 0.279166 obj = -33.098182, rho = -0.050045 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 43 nu = 0.243459 obj = -37.007377, rho = -0.094447 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.212857 obj = -41.381031, rho = -0.157042 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.182398 obj = -46.611956, rho = -0.178594 nSV = 27, nBSV = 15 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 41 nu = 0.167248 obj = -52.608975, rho = -0.067085 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 70 nu = 0.152019 obj = -58.213584, rho = -0.060022 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 73 nu = 0.135349 obj = -63.748957, rho = -0.079761 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.117834 obj = -68.610140, rho = -0.040285 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -6.547573, rho = -0.331227 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 97% (97/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 56 nu = 0.847010 obj = -7.569646, rho = -0.353135 nSV = 86, nBSV = 82 Total nSV = 86 Accuracy = 98% (98/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 46 nu = 0.780729 obj = -8.665898, rho = -0.309132 nSV = 80, nBSV = 76 Total nSV = 80 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 50 nu = 0.695173 obj = -9.853004, rho = -0.283141 nSV = 71, nBSV = 68 Total nSV = 71 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 41 nu = 0.634826 obj = -11.173536, rho = -0.250071 nSV = 66, nBSV = 61 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 44 nu = 0.567827 obj = -12.587338, rho = -0.210103 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 73 nu = 0.509499 obj = -14.034793, rho = -0.159904 nSV = 55, nBSV = 48 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 40 nu = 0.440391 obj = -15.660953, rho = -0.168701 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 37 nu = 0.387814 obj = -17.555971, rho = -0.145195 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 74 nu = 0.335322 obj = -19.672496, rho = -0.153967 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 35 nu = 0.298886 obj = -22.153456, rho = -0.153117 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 42 nu = 0.261174 obj = -24.877528, rho = -0.136880 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 37 nu = 0.229564 obj = -28.086160, rho = -0.152857 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 97 nu = 0.201837 obj = -31.719645, rho = -0.146809 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 136 nu = 0.174593 obj = -36.160636, rho = -0.146744 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *..* optimization finished, #iter = 224 nu = 0.152967 obj = -41.655877, rho = -0.159596 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 78 nu = 0.140000 obj = -48.384703, rho = -0.127814 nSV = 18, nBSV = 13 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.133504 obj = -55.377376, rho = 0.018717 nSV = 16, nBSV = 11 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 65 nu = 0.121027 obj = -62.263044, rho = 0.101686 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 90 nu = 0.104324 obj = -70.137443, rho = 0.102200 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 39 nu = 0.780000 obj = -6.490637, rho = -0.630333 nSV = 78, nBSV = 78 Total nSV = 78 Accuracy = 70% (70/100) (classification) Accuracy = 66.4% (664/1000) (classification) * optimization finished, #iter = 39 nu = 0.780000 obj = -7.813231, rho = -0.528943 nSV = 78, nBSV = 78 Total nSV = 78 Accuracy = 88% (88/100) (classification) Accuracy = 84% (840/1000) (classification) * optimization finished, #iter = 38 nu = 0.760000 obj = -9.252081, rho = -0.453405 nSV = 76, nBSV = 76 Total nSV = 76 Accuracy = 95% (95/100) (classification) Accuracy = 92.5% (925/1000) (classification) * optimization finished, #iter = 39 nu = 0.740000 obj = -10.738553, rho = -0.363022 nSV = 75, nBSV = 73 Total nSV = 75 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 43 nu = 0.680000 obj = -12.297967, rho = -0.303146 nSV = 69, nBSV = 67 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 42 nu = 0.620000 obj = -13.986157, rho = -0.290267 nSV = 63, nBSV = 60 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 45 nu = 0.560950 obj = -15.737177, rho = -0.290124 nSV = 58, nBSV = 54 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 40 nu = 0.504767 obj = -17.583850, rho = -0.333013 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 47 nu = 0.437991 obj = -19.531368, rho = -0.292207 nSV = 49, nBSV = 41 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 62 nu = 0.389490 obj = -21.678326, rho = -0.286757 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 55 nu = 0.336868 obj = -23.924241, rho = -0.264520 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 89 nu = 0.289935 obj = -26.440778, rho = -0.266166 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 68 nu = 0.256964 obj = -29.298470, rho = -0.209496 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) . WARNING: using -h 0 may be faster * optimization finished, #iter = 172 nu = 0.219839 obj = -32.325350, rho = -0.219294 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.188346 obj = -35.828481, rho = -0.228351 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 75 nu = 0.167161 obj = -39.539473, rho = -0.254484 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 82 nu = 0.144859 obj = -43.662722, rho = -0.197658 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 64 nu = 0.124099 obj = -48.248102, rho = -0.159998 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 84 nu = 0.109102 obj = -53.231635, rho = -0.108819 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.095443 obj = -58.104960, rho = -0.031954 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 53 nu = 0.928456 obj = -6.924255, rho = -0.072286 nSV = 94, nBSV = 90 Total nSV = 94 Accuracy = 97% (97/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 44 nu = 0.865760 obj = -8.119751, rho = -0.088842 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 42 nu = 0.821988 obj = -9.422425, rho = -0.136159 nSV = 84, nBSV = 82 Total nSV = 84 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 44 nu = 0.742724 obj = -10.840872, rho = -0.150267 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 47 nu = 0.686153 obj = -12.426436, rho = -0.114305 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 49 nu = 0.618312 obj = -14.177854, rho = -0.065656 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.559955 obj = -16.141322, rho = -0.086916 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 66 nu = 0.500745 obj = -18.301397, rho = -0.078873 nSV = 52, nBSV = 43 Total nSV = 52 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 46 nu = 0.434561 obj = -20.802111, rho = -0.070582 nSV = 48, nBSV = 40 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.386900 obj = -23.801823, rho = -0.122664 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 36 nu = 0.352302 obj = -27.153299, rho = -0.140837 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 76 nu = 0.306901 obj = -31.062083, rho = -0.136563 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 75 nu = 0.271615 obj = -35.878932, rho = -0.121845 nSV = 34, nBSV = 24 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 34 nu = 0.250430 obj = -41.605421, rho = -0.177151 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 56 nu = 0.231533 obj = -47.720711, rho = -0.306100 nSV = 26, nBSV = 21 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 38 nu = 0.208090 obj = -54.731853, rho = -0.360552 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 92 nu = 0.190866 obj = -62.223339, rho = -0.415698 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 39 nu = 0.175248 obj = -69.992222, rho = -0.318532 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 83 nu = 0.155830 obj = -77.915710, rho = -0.277727 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 73 nu = 0.134864 obj = -86.716569, rho = -0.303336 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -6.993214, rho = -0.112320 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 96% (96/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 45 nu = 0.872913 obj = -8.234463, rho = -0.173408 nSV = 89, nBSV = 85 Total nSV = 89 Accuracy = 97% (97/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 44 nu = 0.826937 obj = -9.593886, rho = -0.196320 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 39 nu = 0.760000 obj = -11.078879, rho = -0.187491 nSV = 77, nBSV = 75 Total nSV = 77 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 43 nu = 0.698404 obj = -12.749163, rho = -0.146854 nSV = 70, nBSV = 67 Total nSV = 70 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 38 nu = 0.622585 obj = -14.605133, rho = -0.116755 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 47 nu = 0.575355 obj = -16.655074, rho = -0.053652 nSV = 59, nBSV = 56 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 57 nu = 0.515627 obj = -18.781125, rho = -0.005348 nSV = 56, nBSV = 49 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 48 nu = 0.467028 obj = -21.150079, rho = 0.024496 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 40 nu = 0.411678 obj = -23.647553, rho = 0.002167 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 35 nu = 0.362202 obj = -26.479854, rho = 0.005510 nSV = 38, nBSV = 34 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 36 nu = 0.321117 obj = -29.471844, rho = 0.038736 nSV = 34, nBSV = 30 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 49 nu = 0.289835 obj = -32.455449, rho = -0.015373 nSV = 30, nBSV = 27 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 62 nu = 0.246267 obj = -35.434281, rho = -0.020664 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 72 nu = 0.209837 obj = -38.861636, rho = -0.019699 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.182041 obj = -42.819875, rho = -0.062304 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 276 nu = 0.155730 obj = -46.972763, rho = -0.087269 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.134578 obj = -51.828258, rho = -0.121867 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.118975 obj = -56.691330, rho = -0.201234 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 127 nu = 0.099352 obj = -62.102519, rho = -0.186775 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 47 nu = 0.898911 obj = -6.730382, rho = -0.328330 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 94% (94/100) (classification) Accuracy = 94.8% (948/1000) (classification) * optimization finished, #iter = 48 nu = 0.838627 obj = -7.883286, rho = -0.297561 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 99% (99/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 41 nu = 0.780406 obj = -9.196900, rho = -0.209067 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 40 nu = 0.722437 obj = -10.649825, rho = -0.180571 nSV = 74, nBSV = 72 Total nSV = 74 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 39 nu = 0.671338 obj = -12.248382, rho = -0.191148 nSV = 68, nBSV = 66 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 47 nu = 0.602273 obj = -14.003406, rho = -0.189308 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 66 nu = 0.542308 obj = -15.985377, rho = -0.202532 nSV = 57, nBSV = 51 Total nSV = 57 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 46 nu = 0.483458 obj = -18.268813, rho = -0.224876 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 30 nu = 0.440000 obj = -20.870351, rho = -0.141754 nSV = 46, nBSV = 43 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 66 nu = 0.393338 obj = -23.652621, rho = -0.148393 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 54 nu = 0.347200 obj = -26.930915, rho = -0.154183 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.319934 obj = -30.475976, rho = -0.254256 nSV = 35, nBSV = 26 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 97 nu = 0.277085 obj = -34.424311, rho = -0.229143 nSV = 32, nBSV = 23 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 39 nu = 0.249449 obj = -39.129743, rho = -0.244922 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *..* optimization finished, #iter = 209 nu = 0.225339 obj = -43.957702, rho = -0.228176 nSV = 28, nBSV = 17 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 64 nu = 0.199137 obj = -49.342200, rho = -0.199742 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 93 nu = 0.175441 obj = -55.287208, rho = -0.209780 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) *..* optimization finished, #iter = 281 nu = 0.152448 obj = -62.081188, rho = -0.203900 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..*...* optimization finished, #iter = 510 nu = 0.132403 obj = -69.942377, rho = -0.211539 nSV = 20, nBSV = 9 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 247 nu = 0.118022 obj = -79.204602, rho = -0.226470 nSV = 19, nBSV = 8 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -6.561414, rho = -0.341091 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 47 nu = 0.868025 obj = -7.495010, rho = -0.255459 nSV = 89, nBSV = 85 Total nSV = 89 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 54 nu = 0.792269 obj = -8.460769, rho = -0.202740 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 49 nu = 0.703645 obj = -9.497851, rho = -0.197965 nSV = 73, nBSV = 68 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 39 nu = 0.620000 obj = -10.653863, rho = -0.149192 nSV = 65, nBSV = 60 Total nSV = 65 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 48 nu = 0.550836 obj = -11.923497, rho = -0.146420 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 28 nu = 0.482943 obj = -13.319923, rho = -0.112270 nSV = 50, nBSV = 48 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 45 nu = 0.423527 obj = -14.780514, rho = -0.106148 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 46 nu = 0.369728 obj = -16.460699, rho = -0.111380 nSV = 38, nBSV = 34 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 32 nu = 0.318015 obj = -18.408252, rho = -0.093043 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 36 nu = 0.288761 obj = -20.495071, rho = -0.065417 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 58 nu = 0.253402 obj = -22.514398, rho = -0.042857 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 64 nu = 0.218457 obj = -24.690218, rho = -0.043001 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 42 nu = 0.185987 obj = -27.154635, rho = -0.083669 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 65 nu = 0.157794 obj = -30.068457, rho = -0.095984 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 39 nu = 0.138656 obj = -33.415572, rho = -0.132676 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.128007 obj = -36.478992, rho = -0.128767 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 74 nu = 0.109195 obj = -39.060836, rho = -0.126110 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 94 nu = 0.091966 obj = -41.596009, rho = -0.124655 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 161 nu = 0.075507 obj = -44.568155, rho = -0.101157 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 48 nu = 0.857592 obj = -6.489342, rho = -0.463804 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 92% (92/100) (classification) Accuracy = 89.5% (895/1000) (classification) * optimization finished, #iter = 41 nu = 0.807899 obj = -7.612757, rho = -0.416654 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 92% (92/100) (classification) Accuracy = 92.8% (928/1000) (classification) * optimization finished, #iter = 41 nu = 0.761560 obj = -8.841749, rho = -0.359209 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 94% (94/100) (classification) Accuracy = 95.1% (951/1000) (classification) * optimization finished, #iter = 58 nu = 0.691129 obj = -10.228346, rho = -0.343077 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 94% (94/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 38 nu = 0.621954 obj = -11.835607, rho = -0.307939 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 94% (94/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 45 nu = 0.575886 obj = -13.696957, rho = -0.256156 nSV = 59, nBSV = 56 Total nSV = 59 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 34 nu = 0.520000 obj = -15.772652, rho = -0.263843 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 58 nu = 0.466896 obj = -18.191779, rho = -0.215718 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 29 nu = 0.423606 obj = -21.095061, rho = -0.257824 nSV = 44, nBSV = 41 Total nSV = 44 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 32 nu = 0.391570 obj = -24.253671, rho = -0.330008 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 40 nu = 0.355460 obj = -27.764320, rho = -0.405682 nSV = 38, nBSV = 33 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.320838 obj = -31.657436, rho = -0.370789 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 44 nu = 0.283400 obj = -36.256401, rho = -0.361976 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.265704 obj = -41.254653, rho = -0.252639 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 53 nu = 0.233911 obj = -46.399576, rho = -0.247545 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 75 nu = 0.206687 obj = -52.529114, rho = -0.287979 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.182265 obj = -59.488472, rho = -0.319359 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *...* optimization finished, #iter = 300 nu = 0.167912 obj = -67.057064, rho = -0.358193 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.151844 obj = -74.148393, rho = -0.449885 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 170 nu = 0.129474 obj = -81.829769, rho = -0.456624 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -6.629874, rho = -0.515860 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 77% (77/100) (classification) Accuracy = 76.6% (766/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -7.899521, rho = -0.383072 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 94% (94/100) (classification) Accuracy = 92% (920/1000) (classification) * optimization finished, #iter = 40 nu = 0.780000 obj = -9.254967, rho = -0.328814 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 47 nu = 0.720000 obj = -10.749043, rho = -0.283179 nSV = 75, nBSV = 71 Total nSV = 75 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 39 nu = 0.660000 obj = -12.454532, rho = -0.229575 nSV = 67, nBSV = 65 Total nSV = 67 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.603833 obj = -14.345338, rho = -0.177200 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 46 nu = 0.553948 obj = -16.475505, rho = -0.147158 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 34 nu = 0.495569 obj = -18.875297, rho = -0.157995 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 41 nu = 0.452949 obj = -21.545752, rho = -0.172935 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 99.3% (993/1000) (classification) * optimization finished, #iter = 39 nu = 0.410911 obj = -24.472642, rho = -0.223228 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 47 nu = 0.375367 obj = -27.548970, rho = -0.317232 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 99.4% (994/1000) (classification) * optimization finished, #iter = 58 nu = 0.332344 obj = -30.763389, rho = -0.377241 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 99.4% (994/1000) (classification) * optimization finished, #iter = 95 nu = 0.288351 obj = -34.253008, rho = -0.375060 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 99.5% (995/1000) (classification) * optimization finished, #iter = 68 nu = 0.250955 obj = -38.405388, rho = -0.351659 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 61 nu = 0.220920 obj = -42.955105, rho = -0.437719 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 90 nu = 0.191712 obj = -48.286897, rho = -0.457875 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 77 nu = 0.168010 obj = -54.671011, rho = -0.462149 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 99.3% (993/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.150145 obj = -61.932522, rho = -0.454278 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 99.4% (994/1000) (classification) * optimization finished, #iter = 77 nu = 0.136020 obj = -69.826620, rho = -0.438306 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 99.4% (994/1000) (classification) .* optimization finished, #iter = 161 nu = 0.124348 obj = -77.716018, rho = -0.436057 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 99.4% (994/1000) (classification) * optimization finished, #iter = 47 nu = 0.914592 obj = -6.731600, rho = -0.300355 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 96% (96/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 47 nu = 0.858552 obj = -7.825786, rho = -0.235713 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 43 nu = 0.792590 obj = -9.016196, rho = -0.170867 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 56 nu = 0.731817 obj = -10.289183, rho = -0.113177 nSV = 74, nBSV = 71 Total nSV = 74 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 43 nu = 0.653085 obj = -11.684077, rho = -0.094351 nSV = 68, nBSV = 64 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.596880 obj = -13.195228, rho = -0.015156 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 40 nu = 0.536093 obj = -14.776931, rho = 0.010131 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.467602 obj = -16.467187, rho = 0.002069 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.410089 obj = -18.356975, rho = 0.042490 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 68 nu = 0.355323 obj = -20.458355, rho = 0.082752 nSV = 41, nBSV = 32 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 35 nu = 0.315472 obj = -22.885818, rho = 0.104221 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 27 nu = 0.283155 obj = -25.398880, rho = 0.142229 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 43 nu = 0.247107 obj = -27.893521, rho = 0.144905 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 35 nu = 0.218466 obj = -30.441436, rho = 0.150921 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 70 nu = 0.189140 obj = -32.807985, rho = 0.180087 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.158105 obj = -35.186219, rho = 0.193338 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 62 nu = 0.131019 obj = -38.002122, rho = 0.211229 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 77 nu = 0.114248 obj = -41.093397, rho = 0.282550 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 76 nu = 0.099711 obj = -43.549184, rho = 0.267368 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 84 nu = 0.084668 obj = -45.436003, rho = 0.267294 nSV = 11, nBSV = 5 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -6.696315, rho = 0.097370 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 92.4% (924/1000) (classification) * optimization finished, #iter = 46 nu = 0.859375 obj = -7.809021, rho = -0.005559 nSV = 88, nBSV = 84 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 45 nu = 0.803259 obj = -8.959398, rho = -0.044776 nSV = 83, nBSV = 79 Total nSV = 83 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 53 nu = 0.725057 obj = -10.176770, rho = 0.003612 nSV = 77, nBSV = 71 Total nSV = 77 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.650103 obj = -11.549911, rho = -0.005243 nSV = 67, nBSV = 62 Total nSV = 67 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 34 nu = 0.587049 obj = -13.053356, rho = 0.048149 nSV = 60, nBSV = 57 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 0.528240 obj = -14.659339, rho = 0.075980 nSV = 55, nBSV = 48 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 27 nu = 0.467512 obj = -16.345677, rho = 0.110177 nSV = 48, nBSV = 45 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 53 nu = 0.413610 obj = -18.119911, rho = 0.180231 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 73 nu = 0.356185 obj = -20.097848, rho = 0.190550 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 45 nu = 0.305459 obj = -22.390960, rho = 0.191629 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 36 nu = 0.269948 obj = -25.027938, rho = 0.181826 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 32 nu = 0.237964 obj = -27.861103, rho = 0.138878 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 64 nu = 0.210018 obj = -30.747733, rho = 0.250429 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 78 nu = 0.178293 obj = -34.033417, rho = 0.267611 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 56 nu = 0.155617 obj = -37.931352, rho = 0.265853 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 60 nu = 0.136792 obj = -42.245639, rho = 0.215369 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 49 nu = 0.120561 obj = -46.761266, rho = 0.134926 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.102930 obj = -51.883047, rho = 0.106699 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 58 nu = 0.089579 obj = -57.754024, rho = 0.211694 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 52 nu = 0.860000 obj = -6.750968, rho = -0.469212 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 83% (83/100) (classification) Accuracy = 81.3% (813/1000) (classification) * optimization finished, #iter = 46 nu = 0.847303 obj = -7.959193, rho = -0.342142 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 95% (95/100) (classification) Accuracy = 93.8% (938/1000) (classification) * optimization finished, #iter = 41 nu = 0.800000 obj = -9.281655, rho = -0.359216 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 98% (98/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 47 nu = 0.745597 obj = -10.679131, rho = -0.304449 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 36 nu = 0.680421 obj = -12.151318, rho = -0.281273 nSV = 70, nBSV = 67 Total nSV = 70 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 40 nu = 0.617997 obj = -13.697580, rho = -0.235373 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 40 nu = 0.547175 obj = -15.372714, rho = -0.217612 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 45 nu = 0.489270 obj = -17.209922, rho = -0.207163 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 34 nu = 0.425120 obj = -19.214321, rho = -0.179212 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 64 nu = 0.377528 obj = -21.502771, rho = -0.144405 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 58 nu = 0.327627 obj = -24.012430, rho = -0.121252 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 63 nu = 0.290919 obj = -26.730006, rho = -0.142045 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 48 nu = 0.257505 obj = -29.740584, rho = -0.112733 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.222921 obj = -32.832167, rho = -0.094510 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 45 nu = 0.195763 obj = -36.326649, rho = -0.076435 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 42 nu = 0.169647 obj = -39.964704, rho = -0.072250 nSV = 20, nBSV = 15 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 59 nu = 0.152336 obj = -43.460548, rho = -0.068163 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.129361 obj = -46.734350, rho = -0.143711 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 88 nu = 0.108063 obj = -50.264917, rho = -0.128217 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 59 nu = 0.095363 obj = -53.879248, rho = -0.241518 nSV = 12, nBSV = 7 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.929218 obj = -6.746262, rho = -0.321152 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -7.773007, rho = -0.254871 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.808031 obj = -8.860504, rho = -0.254880 nSV = 82, nBSV = 79 Total nSV = 82 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.731088 obj = -10.022479, rho = -0.217761 nSV = 74, nBSV = 71 Total nSV = 74 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 57 nu = 0.659570 obj = -11.209340, rho = -0.134899 nSV = 68, nBSV = 62 Total nSV = 68 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 39 nu = 0.576599 obj = -12.516852, rho = -0.154465 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 38 nu = 0.510937 obj = -13.974902, rho = -0.141186 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 63 nu = 0.445940 obj = -15.531685, rho = -0.126503 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 51 nu = 0.382734 obj = -17.315363, rho = -0.121204 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.347312 obj = -19.222279, rho = -0.073631 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.296971 obj = -21.188925, rho = -0.050486 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.263166 obj = -23.316879, rho = -0.117454 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 96 nu = 0.224222 obj = -25.580149, rho = -0.054058 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 48 nu = 0.194318 obj = -28.204401, rho = -0.047211 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 46 nu = 0.169894 obj = -30.976822, rho = -0.054656 nSV = 19, nBSV = 15 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 59 nu = 0.150102 obj = -33.673839, rho = 0.029977 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 64 nu = 0.127007 obj = -36.305393, rho = 0.009112 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 96 nu = 0.109215 obj = -38.981179, rho = -0.026846 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.093664 obj = -41.556672, rho = -0.049482 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *..* optimization finished, #iter = 249 nu = 0.078308 obj = -43.717146, rho = -0.019636 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.962014 obj = -7.030356, rho = -0.198007 nSV = 98, nBSV = 96 Total nSV = 98 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -8.150996, rho = -0.134894 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 44 nu = 0.831729 obj = -9.374132, rho = -0.156607 nSV = 84, nBSV = 82 Total nSV = 84 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 41 nu = 0.754346 obj = -10.695178, rho = -0.166987 nSV = 77, nBSV = 74 Total nSV = 77 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.688428 obj = -12.118547, rho = -0.201585 nSV = 71, nBSV = 65 Total nSV = 71 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 36 nu = 0.616587 obj = -13.658905, rho = -0.229621 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 42 nu = 0.540194 obj = -15.384776, rho = -0.230491 nSV = 58, nBSV = 51 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.474945 obj = -17.362463, rho = -0.207449 nSV = 52, nBSV = 45 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 38 nu = 0.419585 obj = -19.647366, rho = -0.209002 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 34 nu = 0.378869 obj = -22.201730, rho = -0.278571 nSV = 39, nBSV = 36 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 41 nu = 0.338672 obj = -24.946484, rho = -0.263484 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.295588 obj = -27.935011, rho = -0.246887 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 64 nu = 0.256531 obj = -31.519442, rho = -0.254705 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 55 nu = 0.225975 obj = -35.696493, rho = -0.228902 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 56 nu = 0.206966 obj = -40.186937, rho = -0.127372 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 62 nu = 0.185193 obj = -44.911748, rho = -0.159486 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 99 nu = 0.162186 obj = -49.887747, rho = -0.215843 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 32 nu = 0.141679 obj = -55.589259, rho = -0.239960 nSV = 17, nBSV = 12 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 58 nu = 0.124180 obj = -61.632816, rho = -0.247668 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.107079 obj = -68.337934, rho = -0.273179 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -7.065698, rho = -0.346404 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 94% (94/100) (classification) Accuracy = 94.5% (945/1000) (classification) * optimization finished, #iter = 48 nu = 0.875923 obj = -8.302013, rho = -0.291138 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 97% (97/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 43 nu = 0.814793 obj = -9.706993, rho = -0.281717 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 97% (97/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 39 nu = 0.769196 obj = -11.286072, rho = -0.315157 nSV = 78, nBSV = 76 Total nSV = 78 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 51 nu = 0.687072 obj = -13.025184, rho = -0.334438 nSV = 73, nBSV = 67 Total nSV = 73 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 41 nu = 0.630647 obj = -15.061571, rho = -0.287274 nSV = 65, nBSV = 60 Total nSV = 65 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 37 nu = 0.577217 obj = -17.404192, rho = -0.254730 nSV = 59, nBSV = 56 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 58 nu = 0.522340 obj = -19.993840, rho = -0.224358 nSV = 56, nBSV = 50 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 54 nu = 0.478915 obj = -22.899813, rho = -0.196064 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 40 nu = 0.432354 obj = -26.148692, rho = -0.214199 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 36 nu = 0.388653 obj = -29.824649, rho = -0.249325 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 56 nu = 0.347267 obj = -33.848639, rho = -0.245656 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 60 nu = 0.309662 obj = -38.299461, rho = -0.206285 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.273824 obj = -43.397660, rho = -0.185013 nSV = 33, nBSV = 23 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 64 nu = 0.252017 obj = -49.124881, rho = -0.161949 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 96 nu = 0.221907 obj = -54.934833, rho = -0.174641 nSV = 29, nBSV = 19 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 60 nu = 0.196429 obj = -61.543574, rho = -0.263729 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 87 nu = 0.173591 obj = -68.512690, rho = -0.335826 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.156495 obj = -75.840643, rho = -0.414793 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.136816 obj = -82.602060, rho = -0.495029 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.926930 obj = -6.986044, rho = -0.252897 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.880000 obj = -8.170782, rho = -0.216581 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 41 nu = 0.820000 obj = -9.480546, rho = -0.180172 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 43 nu = 0.760744 obj = -10.871724, rho = -0.174494 nSV = 79, nBSV = 75 Total nSV = 79 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 46 nu = 0.687842 obj = -12.419590, rho = -0.232411 nSV = 71, nBSV = 66 Total nSV = 71 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 40 nu = 0.615671 obj = -14.164181, rho = -0.286567 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 52 nu = 0.558947 obj = -16.077485, rho = -0.223255 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 43 nu = 0.492822 obj = -18.242407, rho = -0.202221 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 33 nu = 0.433184 obj = -20.802997, rho = -0.187005 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 34 nu = 0.385798 obj = -23.820798, rho = -0.254182 nSV = 41, nBSV = 37 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 35 nu = 0.343773 obj = -27.326787, rho = -0.320113 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 38 nu = 0.310944 obj = -31.525501, rho = -0.283122 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 72 nu = 0.279344 obj = -36.285815, rho = -0.287642 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 58 nu = 0.247672 obj = -42.059716, rho = -0.316876 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 46 nu = 0.229833 obj = -48.877021, rho = -0.475765 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 66 nu = 0.209242 obj = -56.522259, rho = -0.528861 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 82 nu = 0.189501 obj = -65.164338, rho = -0.621144 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.169686 obj = -75.383417, rho = -0.733785 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 88 nu = 0.150917 obj = -87.988371, rho = -0.713429 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.140736 obj = -102.895385, rho = -0.606571 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -7.015030, rho = -0.421069 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 92% (92/100) (classification) Accuracy = 91.1% (911/1000) (classification) * optimization finished, #iter = 44 nu = 0.879327 obj = -8.256944, rho = -0.318315 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 52 nu = 0.816486 obj = -9.612396, rho = -0.282651 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 49 nu = 0.771030 obj = -11.108390, rho = -0.204827 nSV = 79, nBSV = 75 Total nSV = 79 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 42 nu = 0.707904 obj = -12.648953, rho = -0.143063 nSV = 73, nBSV = 69 Total nSV = 73 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 38 nu = 0.633222 obj = -14.359101, rho = -0.125147 nSV = 64, nBSV = 61 Total nSV = 64 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 52 nu = 0.565537 obj = -16.235626, rho = -0.117236 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 83 nu = 0.495916 obj = -18.389427, rho = -0.137831 nSV = 54, nBSV = 46 Total nSV = 54 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 52 nu = 0.440196 obj = -20.965096, rho = -0.095108 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 37 nu = 0.392739 obj = -23.889985, rho = -0.069154 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 50 nu = 0.353039 obj = -27.164637, rho = -0.120420 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 57 nu = 0.315875 obj = -30.881188, rho = -0.183490 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 63 nu = 0.280730 obj = -35.143426, rho = -0.252483 nSV = 32, nBSV = 23 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 56 nu = 0.247232 obj = -40.183985, rho = -0.291066 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 82 nu = 0.221713 obj = -46.002995, rho = -0.372330 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 78 nu = 0.197637 obj = -52.877991, rho = -0.356739 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 91 nu = 0.179206 obj = -60.865298, rho = -0.399642 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 74 nu = 0.162197 obj = -70.041285, rho = -0.321366 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 58 nu = 0.144286 obj = -80.622650, rho = -0.370463 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 42 nu = 0.127710 obj = -93.611049, rho = -0.365728 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 55 nu = 0.922147 obj = -6.626154, rho = -0.108746 nSV = 94, nBSV = 90 Total nSV = 94 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 48 nu = 0.854956 obj = -7.657296, rho = -0.150817 nSV = 88, nBSV = 83 Total nSV = 88 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 47 nu = 0.787408 obj = -8.769014, rho = -0.203326 nSV = 80, nBSV = 76 Total nSV = 80 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 57 nu = 0.712317 obj = -9.982109, rho = -0.141599 nSV = 74, nBSV = 67 Total nSV = 74 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.640000 obj = -11.301052, rho = -0.145635 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 57 nu = 0.570947 obj = -12.759736, rho = -0.151810 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 46 nu = 0.510932 obj = -14.389866, rho = -0.119153 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 45 nu = 0.454184 obj = -16.110100, rho = -0.067851 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 45 nu = 0.402357 obj = -17.956697, rho = -0.073710 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 57 nu = 0.348668 obj = -20.035302, rho = -0.057181 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 42 nu = 0.309896 obj = -22.337799, rho = -0.066275 nSV = 33, nBSV = 29 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 41 nu = 0.273738 obj = -24.754754, rho = -0.141302 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 85 nu = 0.236304 obj = -27.257180, rho = -0.153592 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 92 nu = 0.205791 obj = -30.138015, rho = -0.171610 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.183381 obj = -33.183519, rho = -0.132688 nSV = 20, nBSV = 16 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.155693 obj = -36.180269, rho = -0.063838 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 97 nu = 0.135536 obj = -39.559981, rho = -0.043294 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) ..* optimization finished, #iter = 259 nu = 0.117696 obj = -42.712836, rho = -0.024814 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 299 nu = 0.097926 obj = -46.048952, rho = -0.037474 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.083045 obj = -50.064886, rho = -0.022496 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.980000 obj = -7.659808, rho = -0.247275 nSV = 98, nBSV = 98 Total nSV = 98 Accuracy = 94% (94/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 48 nu = 0.946618 obj = -9.045366, rho = -0.181571 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 94% (94/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.887617 obj = -10.598359, rho = -0.123713 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 96% (96/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 44 nu = 0.828866 obj = -12.329191, rho = -0.127662 nSV = 84, nBSV = 82 Total nSV = 84 Accuracy = 96% (96/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 42 nu = 0.775860 obj = -14.226528, rho = -0.090184 nSV = 78, nBSV = 76 Total nSV = 78 Accuracy = 96% (96/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 47 nu = 0.711429 obj = -16.255135, rho = -0.055697 nSV = 73, nBSV = 70 Total nSV = 73 Accuracy = 96% (96/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 53 nu = 0.644805 obj = -18.438066, rho = -0.078448 nSV = 67, nBSV = 62 Total nSV = 67 Accuracy = 96% (96/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 69 nu = 0.574641 obj = -20.776892, rho = -0.076650 nSV = 61, nBSV = 54 Total nSV = 61 Accuracy = 96% (96/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 50 nu = 0.506488 obj = -23.431937, rho = -0.097169 nSV = 54, nBSV = 47 Total nSV = 54 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 54 nu = 0.459860 obj = -26.377387, rho = -0.076508 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 38 nu = 0.399196 obj = -29.593778, rho = -0.115769 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 96% (96/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 94 nu = 0.353565 obj = -33.275473, rho = -0.163414 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 94 nu = 0.303835 obj = -37.470870, rho = -0.138163 nSV = 35, nBSV = 26 Total nSV = 35 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.266789 obj = -42.502769, rho = -0.081272 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 186 nu = 0.234652 obj = -48.533519, rho = -0.043703 nSV = 30, nBSV = 20 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 150 nu = 0.208231 obj = -55.714575, rho = -0.007423 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 83 nu = 0.195413 obj = -63.788389, rho = 0.030916 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) ..*.* optimization finished, #iter = 319 nu = 0.171144 obj = -72.511604, rho = 0.055843 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 238 nu = 0.150717 obj = -83.074611, rho = 0.080312 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.136144 obj = -95.680302, rho = 0.129506 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.880000 obj = -7.035611, rho = 0.364209 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 83% (83/100) (classification) Accuracy = 80.1% (801/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -8.348647, rho = 0.189828 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 95% (95/100) (classification) Accuracy = 94.2% (942/1000) (classification) * optimization finished, #iter = 46 nu = 0.836066 obj = -9.685518, rho = 0.102531 nSV = 85, nBSV = 81 Total nSV = 85 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 47 nu = 0.780518 obj = -11.131155, rho = 0.045541 nSV = 81, nBSV = 76 Total nSV = 81 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 49 nu = 0.706335 obj = -12.692531, rho = 0.001575 nSV = 73, nBSV = 69 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 44 nu = 0.636919 obj = -14.449655, rho = -0.024378 nSV = 65, nBSV = 62 Total nSV = 65 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 48 nu = 0.568023 obj = -16.358768, rho = -0.018781 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 53 nu = 0.497907 obj = -18.593507, rho = -0.007365 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 70 nu = 0.452921 obj = -21.105228, rho = 0.061172 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 45 nu = 0.399452 obj = -23.894399, rho = 0.013161 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 34 nu = 0.352757 obj = -27.133281, rho = 0.069810 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 50 nu = 0.312003 obj = -30.858800, rho = 0.099139 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 84 nu = 0.275336 obj = -35.316372, rho = 0.049589 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.242470 obj = -40.685362, rho = 0.004526 nSV = 30, nBSV = 20 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 55 nu = 0.218207 obj = -47.271700, rho = 0.063864 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 80 nu = 0.199010 obj = -54.935438, rho = 0.195376 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 162 nu = 0.177646 obj = -64.145776, rho = 0.192739 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 153 nu = 0.159067 obj = -75.760907, rho = 0.185642 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 74 nu = 0.147457 obj = -90.107852, rho = 0.123326 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 61 nu = 0.139816 obj = -107.135331, rho = -0.010774 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 41 nu = 0.760000 obj = -6.361510, rho = -0.653090 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 73% (73/100) (classification) Accuracy = 62.8% (628/1000) (classification) * optimization finished, #iter = 41 nu = 0.760000 obj = -7.673459, rho = -0.557942 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 88% (88/100) (classification) Accuracy = 80.3% (803/1000) (classification) * optimization finished, #iter = 50 nu = 0.759541 obj = -9.075240, rho = -0.437326 nSV = 78, nBSV = 74 Total nSV = 78 Accuracy = 95% (95/100) (classification) Accuracy = 93.1% (931/1000) (classification) * optimization finished, #iter = 42 nu = 0.718675 obj = -10.540442, rho = -0.366090 nSV = 74, nBSV = 70 Total nSV = 74 Accuracy = 96% (96/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 43 nu = 0.657589 obj = -12.126606, rho = -0.366945 nSV = 68, nBSV = 63 Total nSV = 68 Accuracy = 97% (97/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 44 nu = 0.595376 obj = -13.912263, rho = -0.358131 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 31 nu = 0.540000 obj = -15.952947, rho = -0.437436 nSV = 56, nBSV = 53 Total nSV = 56 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 42 nu = 0.491892 obj = -18.165057, rho = -0.473582 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 66 nu = 0.438948 obj = -20.646990, rho = -0.425938 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 96% (96/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.391402 obj = -23.390283, rho = -0.415152 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 86 nu = 0.345422 obj = -26.636165, rho = -0.409125 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 51 nu = 0.311559 obj = -30.338958, rho = -0.393644 nSV = 33, nBSV = 30 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 45 nu = 0.275119 obj = -34.408266, rho = -0.365341 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 41 nu = 0.244422 obj = -39.303204, rho = -0.332894 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.218371 obj = -44.794098, rho = -0.318304 nSV = 24, nBSV = 20 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 55 nu = 0.194691 obj = -51.255841, rho = -0.357124 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 61 nu = 0.175042 obj = -58.457922, rho = -0.344857 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 74 nu = 0.154893 obj = -67.118967, rho = -0.351192 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.142519 obj = -76.871596, rho = -0.201402 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 154 nu = 0.129501 obj = -87.332578, rho = -0.135671 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 51 nu = 0.874234 obj = -6.496875, rho = -0.241160 nSV = 90, nBSV = 86 Total nSV = 90 Accuracy = 95% (95/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 44 nu = 0.826286 obj = -7.580726, rho = -0.224967 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 96% (96/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 47 nu = 0.765334 obj = -8.756740, rho = -0.243456 nSV = 79, nBSV = 74 Total nSV = 79 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 41 nu = 0.701445 obj = -10.066402, rho = -0.247282 nSV = 72, nBSV = 68 Total nSV = 72 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 46 nu = 0.631640 obj = -11.508184, rho = -0.220302 nSV = 66, nBSV = 61 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 38 nu = 0.568185 obj = -13.145762, rho = -0.217484 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 41 nu = 0.513079 obj = -15.004164, rho = -0.197124 nSV = 53, nBSV = 49 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 33 nu = 0.460453 obj = -17.082173, rho = -0.216541 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 46 nu = 0.416423 obj = -19.378890, rho = -0.226322 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 37 nu = 0.373989 obj = -21.831904, rho = -0.201266 nSV = 40, nBSV = 36 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 43 nu = 0.333879 obj = -24.515188, rho = -0.209269 nSV = 35, nBSV = 30 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 57 nu = 0.288251 obj = -27.498910, rho = -0.251261 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 52 nu = 0.256075 obj = -30.928448, rho = -0.227810 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 51 nu = 0.222354 obj = -34.912526, rho = -0.245090 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 62 nu = 0.194644 obj = -39.646143, rho = -0.241181 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 77 nu = 0.177326 obj = -45.007355, rho = -0.253873 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 74 nu = 0.155636 obj = -51.033555, rho = -0.304357 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 46 nu = 0.139584 obj = -57.782816, rho = -0.372927 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 73 nu = 0.122880 obj = -65.445168, rho = -0.376344 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 80 nu = 0.108758 obj = -74.503907, rho = -0.405171 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 54 nu = 0.922144 obj = -6.876114, rho = -0.294846 nSV = 94, nBSV = 91 Total nSV = 94 Accuracy = 92% (92/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 44 nu = 0.853429 obj = -8.049383, rho = -0.276087 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 93% (93/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 46 nu = 0.795222 obj = -9.385688, rho = -0.228668 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 95% (95/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 47 nu = 0.740000 obj = -10.884047, rho = -0.200184 nSV = 76, nBSV = 72 Total nSV = 76 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 37 nu = 0.670284 obj = -12.578778, rho = -0.223972 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 34 nu = 0.603275 obj = -14.553803, rho = -0.250442 nSV = 62, nBSV = 59 Total nSV = 62 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 39 nu = 0.560128 obj = -16.732403, rho = -0.164728 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 31 nu = 0.510102 obj = -19.133931, rho = -0.128827 nSV = 53, nBSV = 49 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 32 nu = 0.470224 obj = -21.727027, rho = -0.106506 nSV = 48, nBSV = 45 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 31 nu = 0.410828 obj = -24.567202, rho = -0.089742 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 31 nu = 0.372188 obj = -27.667438, rho = -0.024893 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 34 nu = 0.327158 obj = -31.102183, rho = -0.002013 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.286888 obj = -34.984781, rho = -0.005518 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 76 nu = 0.248142 obj = -39.611264, rho = 0.009341 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 24 nu = 0.222949 obj = -45.085934, rho = -0.043827 nSV = 26, nBSV = 21 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 40 nu = 0.206762 obj = -50.769619, rho = -0.125628 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 138 nu = 0.181835 obj = -56.717450, rho = -0.091587 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) .*.* optimization finished, #iter = 226 nu = 0.160296 obj = -62.979652, rho = -0.062382 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .* optimization finished, #iter = 133 nu = 0.141502 obj = -69.888632, rho = -0.045713 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) .* optimization finished, #iter = 184 nu = 0.129824 obj = -76.296551, rho = 0.086588 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 48 nu = 0.903504 obj = -6.891955, rho = -0.279894 nSV = 93, nBSV = 90 Total nSV = 93 Accuracy = 91% (91/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 48 nu = 0.860000 obj = -8.106425, rho = -0.237263 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 96% (96/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 54 nu = 0.798034 obj = -9.468485, rho = -0.172966 nSV = 83, nBSV = 77 Total nSV = 83 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 43 nu = 0.740000 obj = -11.018564, rho = -0.127078 nSV = 74, nBSV = 74 Total nSV = 74 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 43 nu = 0.688909 obj = -12.749536, rho = -0.050762 nSV = 70, nBSV = 65 Total nSV = 70 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 45 nu = 0.622362 obj = -14.651741, rho = -0.006925 nSV = 65, nBSV = 62 Total nSV = 65 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 44 nu = 0.571627 obj = -16.785003, rho = 0.005391 nSV = 58, nBSV = 54 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 35 nu = 0.512459 obj = -19.158860, rho = 0.058808 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.461680 obj = -21.845353, rho = 0.120117 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 44 nu = 0.408774 obj = -24.886386, rho = 0.158095 nSV = 43, nBSV = 39 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 44 nu = 0.365482 obj = -28.372447, rho = 0.182766 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 72 nu = 0.328101 obj = -32.298067, rho = 0.214843 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 34 nu = 0.294879 obj = -36.834187, rho = 0.217351 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 40 nu = 0.274107 obj = -41.606028, rho = 0.114092 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 92 nu = 0.242111 obj = -46.347198, rho = 0.057607 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 84 nu = 0.209439 obj = -51.780215, rho = 0.052986 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 75 nu = 0.184822 obj = -57.962685, rho = 0.066727 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 74 nu = 0.164684 obj = -64.495394, rho = 0.035470 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 74 nu = 0.145538 obj = -71.415053, rho = 0.024398 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 52 nu = 0.126742 obj = -78.460934, rho = -0.006904 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 48 nu = 0.912797 obj = -6.652726, rho = 0.098290 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 98% (98/100) (classification) Accuracy = 91.8% (918/1000) (classification) * optimization finished, #iter = 46 nu = 0.858520 obj = -7.689881, rho = 0.008364 nSV = 87, nBSV = 83 Total nSV = 87 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 46 nu = 0.790837 obj = -8.808064, rho = -0.002566 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 40 nu = 0.708121 obj = -10.032944, rho = 0.000350 nSV = 74, nBSV = 69 Total nSV = 74 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 50 nu = 0.635474 obj = -11.392621, rho = 0.048270 nSV = 67, nBSV = 61 Total nSV = 67 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 54 nu = 0.577121 obj = -12.934633, rho = 0.064238 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 34 nu = 0.512976 obj = -14.590857, rho = 0.075576 nSV = 53, nBSV = 49 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 35 nu = 0.455023 obj = -16.434475, rho = 0.026312 nSV = 48, nBSV = 44 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 38 nu = 0.408536 obj = -18.447622, rho = 0.067454 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 74 nu = 0.358660 obj = -20.644846, rho = 0.026708 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 40 nu = 0.313281 obj = -23.139493, rho = -0.009054 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 27 nu = 0.277497 obj = -25.932807, rho = 0.003247 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 27 nu = 0.242569 obj = -29.041119, rho = -0.044050 nSV = 26, nBSV = 22 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 78 nu = 0.220166 obj = -32.329560, rho = -0.170995 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 31 nu = 0.188010 obj = -35.895542, rho = -0.186750 nSV = 22, nBSV = 17 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 52 nu = 0.165792 obj = -39.796398, rho = -0.232352 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 73 nu = 0.146863 obj = -43.968721, rho = -0.156486 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 90 nu = 0.129298 obj = -47.928178, rho = -0.024120 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 98 nu = 0.116645 obj = -51.545582, rho = 0.123879 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.099505 obj = -53.996162, rho = 0.220057 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -7.182731, rho = -0.094370 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 46 nu = 0.907196 obj = -8.390006, rho = -0.073323 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 48 nu = 0.840000 obj = -9.720814, rho = -0.123605 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 40 nu = 0.771964 obj = -11.175820, rho = -0.054644 nSV = 80, nBSV = 76 Total nSV = 80 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 42 nu = 0.712622 obj = -12.762150, rho = -0.092446 nSV = 73, nBSV = 69 Total nSV = 73 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 36 nu = 0.640745 obj = -14.501412, rho = -0.106127 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 39 nu = 0.561164 obj = -16.481252, rho = -0.093820 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 49 nu = 0.505775 obj = -18.707453, rho = -0.143929 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 31 nu = 0.459039 obj = -21.220566, rho = -0.140313 nSV = 48, nBSV = 44 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 32 nu = 0.416326 obj = -23.834701, rho = -0.195299 nSV = 43, nBSV = 39 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 66 nu = 0.370997 obj = -26.331799, rho = -0.158494 nSV = 41, nBSV = 33 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 91 nu = 0.319941 obj = -29.121570, rho = -0.160344 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 60 nu = 0.274771 obj = -32.364060, rho = -0.161844 nSV = 32, nBSV = 23 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.241047 obj = -35.947069, rho = -0.191723 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 88 nu = 0.210008 obj = -39.961215, rho = -0.201220 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.180070 obj = -44.577750, rho = -0.178967 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 141 nu = 0.153961 obj = -50.249166, rho = -0.173246 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 95 nu = 0.137640 obj = -57.054421, rho = -0.193733 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 95 nu = 0.124871 obj = -64.384615, rho = -0.183193 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 168 nu = 0.109913 obj = -72.354585, rho = -0.193412 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 42 nu = 0.660000 obj = -5.919756, rho = -0.790069 nSV = 68, nBSV = 64 Total nSV = 68 Accuracy = 67% (67/100) (classification) Accuracy = 48.1% (481/1000) (classification) * optimization finished, #iter = 42 nu = 0.660000 obj = -7.305651, rho = -0.732490 nSV = 68, nBSV = 64 Total nSV = 68 Accuracy = 68% (68/100) (classification) Accuracy = 49.4% (494/1000) (classification) * optimization finished, #iter = 41 nu = 0.660000 obj = -8.923364, rho = -0.658983 nSV = 68, nBSV = 64 Total nSV = 68 Accuracy = 75% (75/100) (classification) Accuracy = 61.5% (615/1000) (classification) * optimization finished, #iter = 42 nu = 0.660000 obj = -10.743967, rho = -0.565931 nSV = 68, nBSV = 64 Total nSV = 68 Accuracy = 87% (87/100) (classification) Accuracy = 83.1% (831/1000) (classification) * optimization finished, #iter = 54 nu = 0.649677 obj = -12.678005, rho = -0.463155 nSV = 68, nBSV = 63 Total nSV = 68 Accuracy = 96% (96/100) (classification) Accuracy = 93.2% (932/1000) (classification) * optimization finished, #iter = 55 nu = 0.627405 obj = -14.691582, rho = -0.365153 nSV = 65, nBSV = 60 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 40 nu = 0.564996 obj = -16.817213, rho = -0.353729 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 46 nu = 0.516365 obj = -19.225121, rho = -0.307774 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 51 nu = 0.460493 obj = -21.898986, rho = -0.309345 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 88 nu = 0.406955 obj = -25.030334, rho = -0.299645 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.362706 obj = -28.719053, rho = -0.294961 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 42 nu = 0.330529 obj = -32.833309, rho = -0.390035 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 54 nu = 0.296206 obj = -37.629513, rho = -0.423927 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 47 nu = 0.266159 obj = -43.019593, rho = -0.448602 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 42 nu = 0.243854 obj = -49.046315, rho = -0.432000 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 81 nu = 0.217616 obj = -55.686449, rho = -0.366759 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.193637 obj = -63.039725, rho = -0.335712 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 79 nu = 0.171382 obj = -71.656801, rho = -0.320961 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 54 nu = 0.155848 obj = -80.984287, rho = -0.251966 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 94 nu = 0.141921 obj = -90.658743, rho = -0.167682 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 49 nu = 0.940000 obj = -7.322624, rho = 0.201879 nSV = 95, nBSV = 93 Total nSV = 95 Accuracy = 87% (87/100) (classification) Accuracy = 92.4% (924/1000) (classification) * optimization finished, #iter = 48 nu = 0.920000 obj = -8.620431, rho = 0.043731 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 95% (95/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -10.019142, rho = -0.007547 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 43 nu = 0.800000 obj = -11.527652, rho = -0.002855 nSV = 81, nBSV = 79 Total nSV = 81 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 42 nu = 0.719034 obj = -13.197940, rho = -0.006206 nSV = 73, nBSV = 69 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 46 nu = 0.647054 obj = -15.139487, rho = 0.017614 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 49 nu = 0.587580 obj = -17.336043, rho = 0.036342 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 71 nu = 0.530989 obj = -19.786214, rho = 0.060746 nSV = 55, nBSV = 48 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 29 nu = 0.480000 obj = -22.579807, rho = 0.020412 nSV = 49, nBSV = 46 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 52 nu = 0.431867 obj = -25.476120, rho = -0.044068 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 46 nu = 0.382705 obj = -28.789602, rho = -0.109249 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.340681 obj = -32.465419, rho = -0.158021 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 56 nu = 0.302026 obj = -36.483510, rho = -0.147867 nSV = 32, nBSV = 28 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 55 nu = 0.277340 obj = -40.723777, rho = -0.148236 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.241615 obj = -44.695637, rho = -0.116691 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.206615 obj = -49.274116, rho = -0.098223 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 200 nu = 0.182981 obj = -54.142682, rho = -0.109380 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*..* optimization finished, #iter = 322 nu = 0.154369 obj = -59.408876, rho = -0.113308 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..*......* optimization finished, #iter = 841 nu = 0.131676 obj = -65.667817, rho = -0.089201 nSV = 19, nBSV = 8 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 201 nu = 0.113611 obj = -73.147257, rho = -0.082093 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 52 nu = 0.920000 obj = -6.839711, rho = -0.226195 nSV = 93, nBSV = 90 Total nSV = 93 Accuracy = 96% (96/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 49 nu = 0.858571 obj = -8.012482, rho = -0.268743 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 97% (97/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 42 nu = 0.800000 obj = -9.320254, rho = -0.261258 nSV = 80, nBSV = 80 Total nSV = 80 Accuracy = 97% (97/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 49 nu = 0.727063 obj = -10.800777, rho = -0.260062 nSV = 75, nBSV = 71 Total nSV = 75 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 40 nu = 0.680000 obj = -12.427781, rho = -0.190312 nSV = 70, nBSV = 67 Total nSV = 70 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 52 nu = 0.614890 obj = -14.188795, rho = -0.149586 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 68 nu = 0.546830 obj = -16.166547, rho = -0.134734 nSV = 59, nBSV = 51 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 33 nu = 0.491527 obj = -18.529034, rho = -0.173341 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 38 nu = 0.438536 obj = -21.235524, rho = -0.217764 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 30 nu = 0.395609 obj = -24.319686, rho = -0.245916 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 56 nu = 0.363575 obj = -27.774371, rho = -0.154541 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 71 nu = 0.323400 obj = -31.408082, rho = -0.127572 nSV = 38, nBSV = 29 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 49 nu = 0.288589 obj = -35.548732, rho = -0.152332 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.258540 obj = -40.242479, rho = -0.219091 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 63 nu = 0.230731 obj = -45.263134, rho = -0.315947 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 38 nu = 0.206111 obj = -51.008586, rho = -0.390292 nSV = 24, nBSV = 20 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 56 nu = 0.187657 obj = -56.522260, rho = -0.396020 nSV = 20, nBSV = 15 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.164577 obj = -61.851461, rho = -0.462477 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.141662 obj = -67.524725, rho = -0.473908 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 70 nu = 0.123869 obj = -72.913099, rho = -0.486612 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -6.677373, rho = -0.324091 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 91% (91/100) (classification) Accuracy = 93.8% (938/1000) (classification) * optimization finished, #iter = 42 nu = 0.831994 obj = -7.839556, rho = -0.250341 nSV = 84, nBSV = 82 Total nSV = 84 Accuracy = 94% (94/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 39 nu = 0.780000 obj = -9.130866, rho = -0.265558 nSV = 78, nBSV = 78 Total nSV = 78 Accuracy = 96% (96/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 38 nu = 0.720000 obj = -10.553918, rho = -0.203135 nSV = 73, nBSV = 71 Total nSV = 73 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 49 nu = 0.655860 obj = -12.154206, rho = -0.198010 nSV = 68, nBSV = 63 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.591117 obj = -13.998174, rho = -0.186713 nSV = 60, nBSV = 57 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 34 nu = 0.527084 obj = -16.152310, rho = -0.174332 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 41 nu = 0.483351 obj = -18.618005, rho = -0.123931 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 48 nu = 0.431659 obj = -21.481189, rho = -0.173536 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 34 nu = 0.401720 obj = -24.773363, rho = -0.095986 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.362272 obj = -28.351766, rho = -0.131239 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 42 nu = 0.322765 obj = -32.557176, rho = -0.064544 nSV = 35, nBSV = 31 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.293141 obj = -37.400115, rho = -0.045793 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 37 nu = 0.263236 obj = -42.914188, rho = -0.173943 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 31 nu = 0.242139 obj = -49.073939, rho = -0.084344 nSV = 26, nBSV = 22 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.226477 obj = -55.319703, rho = -0.035287 nSV = 24, nBSV = 20 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 56 nu = 0.199464 obj = -61.424265, rho = -0.157437 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 28 nu = 0.180000 obj = -68.283082, rho = -0.009150 nSV = 20, nBSV = 16 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.168208 obj = -72.818773, rho = -0.013789 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 146 nu = 0.141837 obj = -75.961057, rho = -0.014121 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.901606 obj = -6.703350, rho = 0.016211 nSV = 92, nBSV = 89 Total nSV = 92 Accuracy = 96% (96/100) (classification) Accuracy = 93.5% (935/1000) (classification) * optimization finished, #iter = 56 nu = 0.847164 obj = -7.804339, rho = -0.031981 nSV = 87, nBSV = 83 Total nSV = 87 Accuracy = 96% (96/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 53 nu = 0.793352 obj = -9.019180, rho = -0.091277 nSV = 81, nBSV = 76 Total nSV = 81 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 75 nu = 0.722466 obj = -10.321892, rho = -0.028866 nSV = 77, nBSV = 69 Total nSV = 77 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 41 nu = 0.650558 obj = -11.804869, rho = 0.016644 nSV = 68, nBSV = 63 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 38 nu = 0.590217 obj = -13.460747, rho = 0.069991 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 47 nu = 0.524650 obj = -15.293365, rho = 0.073447 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 37 nu = 0.467685 obj = -17.390112, rho = 0.058764 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 48 nu = 0.429542 obj = -19.731041, rho = -0.013570 nSV = 47, nBSV = 40 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 49 nu = 0.376199 obj = -22.276150, rho = -0.007068 nSV = 39, nBSV = 35 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 54 nu = 0.330676 obj = -25.168076, rho = -0.006339 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 30 nu = 0.293616 obj = -28.530667, rho = -0.024801 nSV = 32, nBSV = 28 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 43 nu = 0.262772 obj = -32.372926, rho = 0.075689 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 74 nu = 0.229816 obj = -36.813956, rho = 0.110695 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 90 nu = 0.200697 obj = -42.160540, rho = 0.111644 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 63 nu = 0.184990 obj = -48.495709, rho = 0.048147 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 63 nu = 0.167565 obj = -55.218920, rho = 0.037244 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 40 nu = 0.149471 obj = -62.922308, rho = 0.145196 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 74 nu = 0.132642 obj = -71.542247, rho = 0.246951 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 42 nu = 0.117051 obj = -82.008728, rho = 0.223262 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.880000 obj = -6.973468, rho = -0.478063 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 85% (85/100) (classification) Accuracy = 83.4% (834/1000) (classification) * optimization finished, #iter = 48 nu = 0.879491 obj = -8.247744, rho = -0.335172 nSV = 89, nBSV = 85 Total nSV = 89 Accuracy = 97% (97/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 47 nu = 0.824925 obj = -9.595705, rho = -0.266414 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 42 nu = 0.766850 obj = -11.058573, rho = -0.202621 nSV = 78, nBSV = 76 Total nSV = 78 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 38 nu = 0.704878 obj = -12.626473, rho = -0.194441 nSV = 72, nBSV = 69 Total nSV = 72 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 51 nu = 0.641642 obj = -14.278938, rho = -0.203019 nSV = 67, nBSV = 62 Total nSV = 67 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 84 nu = 0.569540 obj = -16.010418, rho = -0.165197 nSV = 60, nBSV = 52 Total nSV = 60 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 64 nu = 0.506086 obj = -17.976184, rho = -0.153037 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 64 nu = 0.442050 obj = -20.154313, rho = -0.147459 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 74 nu = 0.387859 obj = -22.591649, rho = -0.187313 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 64 nu = 0.341008 obj = -25.381892, rho = -0.174579 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 75 nu = 0.297807 obj = -28.602666, rho = -0.145116 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.266431 obj = -32.280036, rho = -0.151455 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 59 nu = 0.244494 obj = -36.078653, rho = -0.143941 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.213723 obj = -39.825608, rho = -0.166729 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.182948 obj = -44.133835, rho = -0.140903 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 60 nu = 0.159188 obj = -48.917010, rho = -0.228449 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.136983 obj = -54.321496, rho = -0.277715 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 263 nu = 0.116040 obj = -60.974278, rho = -0.279277 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.105168 obj = -68.980312, rho = -0.331590 nSV = 13, nBSV = 9 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -6.890644, rho = -0.400006 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 91% (91/100) (classification) Accuracy = 93.9% (939/1000) (classification) * optimization finished, #iter = 46 nu = 0.856960 obj = -8.066784, rho = -0.425113 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 92% (92/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 43 nu = 0.810870 obj = -9.397681, rho = -0.380769 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 96% (96/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 43 nu = 0.756889 obj = -10.789445, rho = -0.328464 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 96% (96/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 37 nu = 0.681237 obj = -12.306925, rho = -0.336678 nSV = 70, nBSV = 67 Total nSV = 70 Accuracy = 96% (96/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 59 nu = 0.617994 obj = -13.957910, rho = -0.272415 nSV = 65, nBSV = 59 Total nSV = 65 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 35 nu = 0.549438 obj = -15.816577, rho = -0.245034 nSV = 56, nBSV = 53 Total nSV = 56 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 58 nu = 0.487148 obj = -17.897106, rho = -0.181347 nSV = 53, nBSV = 46 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 35 nu = 0.431606 obj = -20.292533, rho = -0.209700 nSV = 45, nBSV = 41 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.386277 obj = -23.058598, rho = -0.192114 nSV = 40, nBSV = 36 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 98 nu = 0.340689 obj = -26.107440, rho = -0.157404 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.300689 obj = -29.746745, rho = -0.116377 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 49 nu = 0.264902 obj = -34.102284, rho = -0.104065 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 38 nu = 0.240892 obj = -39.257241, rho = -0.068885 nSV = 27, nBSV = 22 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 59 nu = 0.220339 obj = -44.843865, rho = -0.014906 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 60 nu = 0.194309 obj = -51.224315, rho = -0.017139 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 46 nu = 0.179100 obj = -58.457624, rho = -0.022307 nSV = 19, nBSV = 14 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 154 nu = 0.158895 obj = -66.091067, rho = 0.014112 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 91 nu = 0.138492 obj = -75.412882, rho = -0.011860 nSV = 16, nBSV = 11 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 87 nu = 0.132902 obj = -85.071778, rho = 0.023816 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -6.375641, rho = -0.328899 nSV = 89, nBSV = 87 Total nSV = 89 Accuracy = 98% (98/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 44 nu = 0.813661 obj = -7.385514, rho = -0.301254 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 41 nu = 0.760000 obj = -8.492610, rho = -0.229548 nSV = 76, nBSV = 76 Total nSV = 76 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 49 nu = 0.691459 obj = -9.660230, rho = -0.206722 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 48 nu = 0.624832 obj = -10.912343, rho = -0.246557 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 45 nu = 0.559345 obj = -12.249852, rho = -0.288506 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 59 nu = 0.492486 obj = -13.685397, rho = -0.258457 nSV = 52, nBSV = 45 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 27 nu = 0.442551 obj = -15.272706, rho = -0.296461 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 24 nu = 0.382099 obj = -16.973228, rho = -0.253031 nSV = 40, nBSV = 37 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 31 nu = 0.331874 obj = -18.820005, rho = -0.205302 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 40 nu = 0.300491 obj = -20.732649, rho = -0.222573 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 45 nu = 0.255688 obj = -22.664293, rho = -0.217745 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 43 nu = 0.223573 obj = -24.761765, rho = -0.172941 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 40 nu = 0.196099 obj = -26.713931, rho = -0.257294 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.166529 obj = -28.522557, rho = -0.259457 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 155 nu = 0.138095 obj = -30.466514, rho = -0.240762 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 182 nu = 0.117839 obj = -32.492718, rho = -0.141959 nSV = 18, nBSV = 7 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 259 nu = 0.096797 obj = -34.637317, rho = -0.138929 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .*.* optimization finished, #iter = 228 nu = 0.079692 obj = -37.239074, rho = -0.130345 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 166 nu = 0.067058 obj = -40.351242, rho = -0.156216 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 48 nu = 0.800000 obj = -6.511289, rho = -0.533172 nSV = 82, nBSV = 78 Total nSV = 82 Accuracy = 76% (76/100) (classification) Accuracy = 72.9% (729/1000) (classification) * optimization finished, #iter = 48 nu = 0.800000 obj = -7.776866, rho = -0.405133 nSV = 82, nBSV = 78 Total nSV = 82 Accuracy = 92% (92/100) (classification) Accuracy = 89.2% (892/1000) (classification) * optimization finished, #iter = 44 nu = 0.780000 obj = -9.095623, rho = -0.275906 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 97% (97/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 39 nu = 0.740000 obj = -10.447900, rho = -0.176197 nSV = 75, nBSV = 73 Total nSV = 75 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 46 nu = 0.667818 obj = -11.875662, rho = -0.146835 nSV = 68, nBSV = 65 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 31 nu = 0.600000 obj = -13.445688, rho = -0.143875 nSV = 60, nBSV = 60 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 41 nu = 0.537529 obj = -15.129289, rho = -0.127985 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 38 nu = 0.472744 obj = -17.024462, rho = -0.110293 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 48 nu = 0.419836 obj = -19.106776, rho = -0.133918 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 68 nu = 0.369049 obj = -21.401724, rho = -0.179761 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 48 nu = 0.324171 obj = -24.023437, rho = -0.188621 nSV = 35, nBSV = 30 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 47 nu = 0.289718 obj = -26.918525, rho = -0.160918 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 61 nu = 0.256798 obj = -29.834851, rho = -0.030573 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 36 nu = 0.232980 obj = -32.911671, rho = -0.079407 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 72 nu = 0.199791 obj = -35.596967, rho = -0.058481 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.170944 obj = -38.584124, rho = -0.054366 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 96 nu = 0.142882 obj = -41.828173, rho = -0.082908 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 78 nu = 0.120677 obj = -45.710020, rho = -0.078254 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 87 nu = 0.104504 obj = -50.132017, rho = -0.118593 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.091621 obj = -54.454521, rho = -0.180486 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -6.763899, rho = -0.494514 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 92% (92/100) (classification) Accuracy = 90.3% (903/1000) (classification) * optimization finished, #iter = 52 nu = 0.842955 obj = -7.909101, rho = -0.423082 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 96% (96/100) (classification) Accuracy = 92.7% (927/1000) (classification) * optimization finished, #iter = 46 nu = 0.791089 obj = -9.219882, rho = -0.403979 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 95% (95/100) (classification) Accuracy = 94.8% (948/1000) (classification) * optimization finished, #iter = 40 nu = 0.736956 obj = -10.650388, rho = -0.353880 nSV = 74, nBSV = 72 Total nSV = 74 Accuracy = 95% (95/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 41 nu = 0.668651 obj = -12.168571, rho = -0.357825 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 95% (95/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 51 nu = 0.609419 obj = -13.847369, rho = -0.340563 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 94% (94/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 51 nu = 0.544777 obj = -15.699700, rho = -0.312522 nSV = 57, nBSV = 51 Total nSV = 57 Accuracy = 95% (95/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 41 nu = 0.482349 obj = -17.807939, rho = -0.314682 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 96% (96/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 33 nu = 0.430910 obj = -20.175546, rho = -0.314731 nSV = 45, nBSV = 42 Total nSV = 45 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 60 nu = 0.380769 obj = -22.864245, rho = -0.380175 nSV = 42, nBSV = 34 Total nSV = 42 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 58 nu = 0.335301 obj = -26.048233, rho = -0.375138 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 96% (96/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 83 nu = 0.294426 obj = -29.834223, rho = -0.330041 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 96% (96/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 58 nu = 0.264343 obj = -34.396756, rho = -0.307618 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 47 nu = 0.237703 obj = -39.731899, rho = -0.335851 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 61 nu = 0.214464 obj = -46.014553, rho = -0.357495 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 55 nu = 0.194151 obj = -53.465509, rho = -0.344283 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.175850 obj = -62.163641, rho = -0.396024 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 80 nu = 0.157793 obj = -72.758397, rho = -0.421417 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 51 nu = 0.144975 obj = -85.682164, rho = -0.459014 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 84 nu = 0.140263 obj = -99.996173, rho = -0.538028 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -6.580554, rho = -0.436143 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 87% (87/100) (classification) Accuracy = 81.3% (813/1000) (classification) * optimization finished, #iter = 43 nu = 0.822538 obj = -7.756336, rho = -0.322080 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 96% (96/100) (classification) Accuracy = 92.1% (921/1000) (classification) * optimization finished, #iter = 50 nu = 0.781023 obj = -9.005214, rho = -0.239250 nSV = 80, nBSV = 76 Total nSV = 80 Accuracy = 98% (98/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 36 nu = 0.720000 obj = -10.379447, rho = -0.224599 nSV = 72, nBSV = 72 Total nSV = 72 Accuracy = 98% (98/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 51 nu = 0.650266 obj = -11.875586, rho = -0.265658 nSV = 67, nBSV = 62 Total nSV = 67 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 44 nu = 0.582688 obj = -13.594832, rho = -0.263266 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 59 nu = 0.529513 obj = -15.499985, rho = -0.235948 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.481190 obj = -17.577571, rho = -0.194163 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.423583 obj = -19.900062, rho = -0.160427 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 34 nu = 0.380000 obj = -22.566016, rho = -0.118511 nSV = 40, nBSV = 37 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 35 nu = 0.348101 obj = -25.377213, rho = -0.172141 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 31 nu = 0.311920 obj = -28.249710, rho = -0.280065 nSV = 32, nBSV = 29 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 46 nu = 0.271494 obj = -31.207627, rho = -0.282048 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 89 nu = 0.229657 obj = -34.588504, rho = -0.255350 nSV = 29, nBSV = 19 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.205413 obj = -38.506842, rho = -0.279487 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.175586 obj = -42.706558, rho = -0.239758 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 77 nu = 0.149678 obj = -47.820331, rho = -0.238068 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.133985 obj = -53.679031, rho = -0.315025 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.117311 obj = -60.058991, rho = -0.323371 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 228 nu = 0.102892 obj = -67.026481, rho = -0.294330 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.920553 obj = -6.754463, rho = -0.240731 nSV = 94, nBSV = 91 Total nSV = 94 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 44 nu = 0.875035 obj = -7.807580, rho = -0.210636 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 42 nu = 0.796802 obj = -8.969222, rho = -0.146813 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 39 nu = 0.738008 obj = -10.212502, rho = -0.186267 nSV = 74, nBSV = 72 Total nSV = 74 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.651840 obj = -11.560080, rho = -0.162684 nSV = 68, nBSV = 62 Total nSV = 68 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 38 nu = 0.578528 obj = -13.087223, rho = -0.125362 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 45 nu = 0.523402 obj = -14.731579, rho = -0.111413 nSV = 55, nBSV = 48 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 37 nu = 0.461670 obj = -16.562278, rho = -0.150036 nSV = 48, nBSV = 45 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 40 nu = 0.406113 obj = -18.600169, rho = -0.174167 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 53 nu = 0.362544 obj = -20.853238, rho = -0.154806 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 56 nu = 0.323049 obj = -23.304350, rho = -0.118213 nSV = 35, nBSV = 30 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 50 nu = 0.284867 obj = -25.824796, rho = -0.118331 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 66 nu = 0.250380 obj = -28.491366, rho = -0.133023 nSV = 27, nBSV = 22 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 157 nu = 0.213762 obj = -31.296530, rho = -0.116290 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 64 nu = 0.183118 obj = -34.655053, rho = -0.099146 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 150 nu = 0.161883 obj = -38.183469, rho = -0.118768 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 57 nu = 0.139157 obj = -42.085511, rho = -0.145010 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 77 nu = 0.122073 obj = -46.289684, rho = -0.202138 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.102967 obj = -50.881076, rho = -0.199372 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 84 nu = 0.090252 obj = -56.218346, rho = -0.115380 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.960000 obj = -7.148439, rho = -0.271416 nSV = 97, nBSV = 95 Total nSV = 97 Accuracy = 97% (97/100) (classification) Accuracy = 94.2% (942/1000) (classification) * optimization finished, #iter = 49 nu = 0.895615 obj = -8.354932, rho = -0.293978 nSV = 91, nBSV = 86 Total nSV = 91 Accuracy = 98% (98/100) (classification) Accuracy = 94.6% (946/1000) (classification) * optimization finished, #iter = 43 nu = 0.841169 obj = -9.702857, rho = -0.186937 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 67 nu = 0.789894 obj = -11.062989, rho = -0.100492 nSV = 81, nBSV = 76 Total nSV = 81 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 40 nu = 0.715565 obj = -12.570983, rho = -0.070233 nSV = 72, nBSV = 68 Total nSV = 72 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 65 nu = 0.629609 obj = -14.224879, rho = -0.047641 nSV = 66, nBSV = 60 Total nSV = 66 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 56 nu = 0.558627 obj = -16.116491, rho = -0.051512 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 34 nu = 0.502897 obj = -18.227902, rho = -0.069557 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 42 nu = 0.447389 obj = -20.541804, rho = -0.063964 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 70 nu = 0.396850 obj = -23.065823, rho = -0.092304 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.345381 obj = -25.954816, rho = -0.117386 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 51 nu = 0.304773 obj = -29.364307, rho = -0.152422 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 54 nu = 0.273045 obj = -33.160798, rho = -0.216941 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 98 nu = 0.241315 obj = -37.355802, rho = -0.229807 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.211340 obj = -42.173188, rho = -0.206620 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.193283 obj = -47.395553, rho = -0.207138 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *..* optimization finished, #iter = 229 nu = 0.173687 obj = -52.532959, rho = -0.154003 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 59 nu = 0.151205 obj = -57.896971, rho = -0.281513 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.131044 obj = -63.815537, rho = -0.289807 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 90 nu = 0.114017 obj = -70.130931, rho = -0.241665 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 46 nu = 0.903146 obj = -6.629903, rho = -0.199055 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 98% (98/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 46 nu = 0.841586 obj = -7.697406, rho = -0.155831 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 43 nu = 0.783734 obj = -8.857956, rho = -0.092697 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 46 nu = 0.699833 obj = -10.155521, rho = -0.080504 nSV = 72, nBSV = 67 Total nSV = 72 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 42 nu = 0.637603 obj = -11.655851, rho = -0.021335 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 37 nu = 0.577778 obj = -13.282740, rho = -0.002891 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 35 nu = 0.526251 obj = -15.106454, rho = -0.058073 nSV = 54, nBSV = 51 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 41 nu = 0.482135 obj = -16.974740, rho = -0.057458 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 44 nu = 0.429078 obj = -18.907943, rho = 0.000898 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 60 nu = 0.372374 obj = -20.952813, rho = -0.048004 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.322769 obj = -23.217987, rho = -0.064785 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 40 nu = 0.280000 obj = -25.853650, rho = -0.043084 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.244047 obj = -28.692168, rho = 0.054921 nSV = 29, nBSV = 19 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 59 nu = 0.208736 obj = -32.138021, rho = 0.098138 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 39 nu = 0.181841 obj = -36.174063, rho = 0.050933 nSV = 20, nBSV = 15 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 43 nu = 0.164613 obj = -40.804570, rho = -0.048897 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 54 nu = 0.149798 obj = -45.307179, rho = -0.144254 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.130077 obj = -49.876378, rho = -0.184771 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 96 nu = 0.110513 obj = -55.159626, rho = -0.212567 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 119 nu = 0.097134 obj = -61.074859, rho = -0.187474 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.852326 obj = -6.289726, rho = -0.214365 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 95% (95/100) (classification) Accuracy = 93.6% (936/1000) (classification) * optimization finished, #iter = 43 nu = 0.798030 obj = -7.330421, rho = -0.209029 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 98% (98/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 42 nu = 0.725117 obj = -8.511340, rho = -0.195035 nSV = 74, nBSV = 70 Total nSV = 74 Accuracy = 98% (98/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 41 nu = 0.664499 obj = -9.869068, rho = -0.239688 nSV = 70, nBSV = 65 Total nSV = 70 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 37 nu = 0.613446 obj = -11.430606, rho = -0.263569 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 36 nu = 0.570284 obj = -13.108440, rho = -0.285686 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 41 nu = 0.522199 obj = -14.849170, rho = -0.230747 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 42 nu = 0.469819 obj = -16.690846, rho = -0.212703 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 53 nu = 0.413450 obj = -18.651128, rho = -0.190750 nSV = 45, nBSV = 37 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 35 nu = 0.358879 obj = -20.916970, rho = -0.168982 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 32 nu = 0.317644 obj = -23.543236, rho = -0.150922 nSV = 33, nBSV = 30 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 57 nu = 0.279277 obj = -26.400142, rho = -0.136020 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 41 nu = 0.247833 obj = -29.633706, rho = -0.147049 nSV = 27, nBSV = 22 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 71 nu = 0.223508 obj = -32.895014, rho = -0.210565 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 84 nu = 0.198620 obj = -36.211192, rho = -0.302170 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 143 nu = 0.171441 obj = -39.594257, rho = -0.320664 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 95 nu = 0.148111 obj = -43.051522, rho = -0.342109 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 65 nu = 0.127994 obj = -46.705722, rho = -0.306340 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.114451 obj = -49.404896, rho = -0.279301 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 81 nu = 0.095116 obj = -51.580555, rho = -0.299985 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 99.3% (993/1000) (classification) * optimization finished, #iter = 51 nu = 0.932580 obj = -6.729180, rho = -0.426616 nSV = 96, nBSV = 92 Total nSV = 96 Accuracy = 98% (98/100) (classification) Accuracy = 94% (940/1000) (classification) * optimization finished, #iter = 44 nu = 0.860000 obj = -7.789620, rho = -0.411134 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 98% (98/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 53 nu = 0.787459 obj = -8.951147, rho = -0.367179 nSV = 81, nBSV = 76 Total nSV = 81 Accuracy = 98% (98/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 44 nu = 0.720740 obj = -10.276694, rho = -0.348872 nSV = 74, nBSV = 71 Total nSV = 74 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 42 nu = 0.658452 obj = -11.685603, rho = -0.330698 nSV = 67, nBSV = 64 Total nSV = 67 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 39 nu = 0.589912 obj = -13.232058, rho = -0.365705 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 35 nu = 0.532834 obj = -14.915719, rho = -0.382567 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 53 nu = 0.470866 obj = -16.710509, rho = -0.402643 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 53 nu = 0.417479 obj = -18.693550, rho = -0.379821 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 53 nu = 0.359599 obj = -20.895650, rho = -0.386730 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 84 nu = 0.312204 obj = -23.491397, rho = -0.407822 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 79 nu = 0.277820 obj = -26.538558, rho = -0.384550 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 41 nu = 0.250234 obj = -29.915695, rho = -0.317336 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.226192 obj = -33.152938, rho = -0.210059 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 66 nu = 0.191750 obj = -36.787887, rho = -0.215287 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 99 nu = 0.168792 obj = -41.011495, rho = -0.173651 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 229 nu = 0.143089 obj = -45.888122, rho = -0.177122 nSV = 21, nBSV = 10 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 232 nu = 0.124274 obj = -51.867237, rho = -0.169730 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 130 nu = 0.108811 obj = -59.157064, rho = -0.171497 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.097821 obj = -67.768832, rho = -0.160758 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -7.078843, rho = 0.235828 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 82% (82/100) (classification) Accuracy = 81.8% (818/1000) (classification) * optimization finished, #iter = 46 nu = 0.857714 obj = -8.433229, rho = 0.166383 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 92% (92/100) (classification) Accuracy = 90.7% (907/1000) (classification) * optimization finished, #iter = 43 nu = 0.820000 obj = -9.963272, rho = 0.093298 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 96% (96/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 43 nu = 0.769969 obj = -11.654026, rho = 0.082900 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 51 nu = 0.718628 obj = -13.525201, rho = 0.093070 nSV = 74, nBSV = 70 Total nSV = 74 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 45 nu = 0.666019 obj = -15.554317, rho = 0.081007 nSV = 68, nBSV = 65 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 50 nu = 0.602111 obj = -17.773064, rho = 0.038148 nSV = 63, nBSV = 57 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 35 nu = 0.541110 obj = -20.305224, rho = 0.043593 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 48 nu = 0.484619 obj = -23.143356, rho = 0.057596 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 33 nu = 0.440000 obj = -26.359657, rho = 0.095365 nSV = 45, nBSV = 43 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 32 nu = 0.400000 obj = -29.814242, rho = 0.140613 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 64 nu = 0.365008 obj = -33.237907, rho = 0.192948 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 81 nu = 0.324248 obj = -36.491083, rho = 0.207316 nSV = 36, nBSV = 27 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 83 nu = 0.275839 obj = -40.017410, rho = 0.201093 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 99 nu = 0.240973 obj = -43.785726, rho = 0.229287 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.207439 obj = -47.690170, rho = 0.227563 nSV = 26, nBSV = 15 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 91 nu = 0.175203 obj = -52.101121, rho = 0.282604 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 243 nu = 0.149376 obj = -57.227276, rho = 0.290192 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 189 nu = 0.126961 obj = -63.298769, rho = 0.298404 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *..* optimization finished, #iter = 217 nu = 0.114780 obj = -69.302305, rho = 0.249496 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 37 nu = 0.700000 obj = -5.836916, rho = -0.725070 nSV = 71, nBSV = 69 Total nSV = 71 Accuracy = 78% (78/100) (classification) Accuracy = 60.2% (602/1000) (classification) * optimization finished, #iter = 37 nu = 0.700000 obj = -7.031336, rho = -0.649664 nSV = 71, nBSV = 69 Total nSV = 71 Accuracy = 90% (90/100) (classification) Accuracy = 76.5% (765/1000) (classification) * optimization finished, #iter = 35 nu = 0.680000 obj = -8.345620, rho = -0.598931 nSV = 68, nBSV = 68 Total nSV = 68 Accuracy = 95% (95/100) (classification) Accuracy = 86.2% (862/1000) (classification) * optimization finished, #iter = 35 nu = 0.659187 obj = -9.733212, rho = -0.519116 nSV = 67, nBSV = 64 Total nSV = 67 Accuracy = 97% (97/100) (classification) Accuracy = 93% (930/1000) (classification) * optimization finished, #iter = 34 nu = 0.618143 obj = -11.183356, rho = -0.469399 nSV = 62, nBSV = 60 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 67 nu = 0.550051 obj = -12.745623, rho = -0.465292 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 37 nu = 0.492633 obj = -14.553184, rho = -0.443361 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 30 nu = 0.440363 obj = -16.600759, rho = -0.419598 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 33 nu = 0.399335 obj = -18.958746, rho = -0.506381 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 28 nu = 0.360000 obj = -21.573175, rho = -0.442951 nSV = 37, nBSV = 34 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 33 nu = 0.316637 obj = -24.509269, rho = -0.453536 nSV = 35, nBSV = 30 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 67 nu = 0.288296 obj = -27.819038, rho = -0.497893 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 42 nu = 0.252253 obj = -31.623107, rho = -0.523431 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.223140 obj = -36.032573, rho = -0.520536 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 50 nu = 0.201723 obj = -41.209910, rho = -0.705791 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 39 nu = 0.182519 obj = -46.872122, rho = -0.654399 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 69 nu = 0.165082 obj = -53.160121, rho = -0.564461 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 53 nu = 0.154238 obj = -59.216843, rho = -0.334085 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 83 nu = 0.138276 obj = -64.597244, rho = -0.157324 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 92 nu = 0.119229 obj = -69.604725, rho = -0.296290 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -7.328774, rho = -0.216019 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 49 nu = 0.939130 obj = -8.572578, rho = -0.340241 nSV = 95, nBSV = 92 Total nSV = 95 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 49 nu = 0.860000 obj = -9.902220, rho = -0.304359 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 41 nu = 0.785639 obj = -11.422245, rho = -0.281213 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 43 nu = 0.723193 obj = -13.060023, rho = -0.330359 nSV = 75, nBSV = 71 Total nSV = 75 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 44 nu = 0.651677 obj = -14.863025, rho = -0.306831 nSV = 67, nBSV = 64 Total nSV = 67 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.582902 obj = -16.863466, rho = -0.325677 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 75 nu = 0.521997 obj = -19.085743, rho = -0.313088 nSV = 56, nBSV = 49 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 91 nu = 0.464002 obj = -21.568326, rho = -0.339426 nSV = 49, nBSV = 42 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 85 nu = 0.404799 obj = -24.465435, rho = -0.349653 nSV = 46, nBSV = 37 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 86 nu = 0.365507 obj = -27.879906, rho = -0.315530 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 63 nu = 0.323631 obj = -31.692371, rho = -0.302292 nSV = 36, nBSV = 27 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 68 nu = 0.286593 obj = -36.099894, rho = -0.300595 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.256000 obj = -41.195038, rho = -0.240145 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 66 nu = 0.228126 obj = -47.139252, rho = -0.200220 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 55 nu = 0.208362 obj = -53.796011, rho = -0.300640 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 29 nu = 0.191503 obj = -60.908386, rho = -0.279951 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 175 nu = 0.170247 obj = -68.138781, rho = -0.212960 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 94 nu = 0.153804 obj = -75.736724, rho = -0.220052 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 77 nu = 0.138610 obj = -83.114699, rho = -0.330718 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 45 nu = 0.840000 obj = -6.683973, rho = -0.472415 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 87% (87/100) (classification) Accuracy = 83.4% (834/1000) (classification) * optimization finished, #iter = 46 nu = 0.837614 obj = -7.917579, rho = -0.331413 nSV = 85, nBSV = 81 Total nSV = 85 Accuracy = 94% (94/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 40 nu = 0.780000 obj = -9.248903, rho = -0.263934 nSV = 79, nBSV = 77 Total nSV = 79 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 43 nu = 0.728697 obj = -10.737471, rho = -0.257745 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 44 nu = 0.673116 obj = -12.317320, rho = -0.218384 nSV = 70, nBSV = 65 Total nSV = 70 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 39 nu = 0.616113 obj = -14.077776, rho = -0.223660 nSV = 62, nBSV = 60 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.545839 obj = -16.018165, rho = -0.239050 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 50 nu = 0.488994 obj = -18.216966, rho = -0.202120 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.455370 obj = -20.533381, rho = -0.276364 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.394912 obj = -22.983312, rho = -0.296343 nSV = 45, nBSV = 36 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 33 nu = 0.354511 obj = -25.797677, rho = -0.262846 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 60 nu = 0.305694 obj = -28.847495, rho = -0.269392 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 61 nu = 0.264678 obj = -32.492592, rho = -0.250159 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 48 nu = 0.236419 obj = -36.746331, rho = -0.285402 nSV = 26, nBSV = 21 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 63 nu = 0.205154 obj = -41.581587, rho = -0.296297 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.182979 obj = -47.387647, rho = -0.339840 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*...* optimization finished, #iter = 478 nu = 0.165693 obj = -53.774006, rho = -0.382626 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 144 nu = 0.145937 obj = -60.944091, rho = -0.379823 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 130 nu = 0.127357 obj = -69.394259, rho = -0.387930 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.116648 obj = -79.228076, rho = -0.461281 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 45 nu = 0.840000 obj = -6.790924, rho = 0.184422 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 81% (81/100) (classification) Accuracy = 79.1% (791/1000) (classification) * optimization finished, #iter = 45 nu = 0.840000 obj = -8.091130, rho = -0.039270 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 89% (89/100) (classification) Accuracy = 91.9% (919/1000) (classification) * optimization finished, #iter = 47 nu = 0.795305 obj = -9.477084, rho = -0.159612 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 90% (90/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 42 nu = 0.728136 obj = -11.057700, rho = -0.159595 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 94% (94/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 40 nu = 0.688386 obj = -12.841361, rho = -0.252784 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 96% (96/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 49 nu = 0.618481 obj = -14.826164, rho = -0.237889 nSV = 65, nBSV = 60 Total nSV = 65 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 51 nu = 0.567205 obj = -17.121405, rho = -0.181256 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 42 nu = 0.512142 obj = -19.751327, rho = -0.210455 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 43 nu = 0.464397 obj = -22.767737, rho = -0.175708 nSV = 48, nBSV = 44 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 46 nu = 0.426575 obj = -26.131262, rho = -0.180729 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 54 nu = 0.385022 obj = -29.784015, rho = -0.238955 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 47 nu = 0.352272 obj = -33.912072, rho = -0.105653 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 55 nu = 0.313375 obj = -38.359708, rho = -0.095411 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 90 nu = 0.282812 obj = -43.047755, rho = -0.117571 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 58 nu = 0.246378 obj = -48.364183, rho = -0.118494 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 68 nu = 0.225620 obj = -53.941471, rho = 0.017333 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 94 nu = 0.198401 obj = -59.555667, rho = 0.121513 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.168498 obj = -65.756915, rho = 0.167348 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.146980 obj = -72.853587, rho = 0.257990 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 143 nu = 0.130124 obj = -80.362523, rho = 0.308547 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 52 nu = 0.897260 obj = -6.564587, rho = -0.081751 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 96% (96/100) (classification) Accuracy = 92.8% (928/1000) (classification) * optimization finished, #iter = 49 nu = 0.849415 obj = -7.605411, rho = -0.154791 nSV = 86, nBSV = 82 Total nSV = 86 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 43 nu = 0.780000 obj = -8.720686, rho = -0.110643 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 45 nu = 0.698701 obj = -9.945045, rho = -0.093484 nSV = 72, nBSV = 67 Total nSV = 72 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 44 nu = 0.622352 obj = -11.375773, rho = -0.099067 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 43 nu = 0.562519 obj = -12.987765, rho = -0.089020 nSV = 58, nBSV = 54 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 39 nu = 0.513303 obj = -14.767134, rho = -0.095225 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 31 nu = 0.463392 obj = -16.695413, rho = -0.066879 nSV = 48, nBSV = 45 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 41 nu = 0.409964 obj = -18.766068, rho = -0.130086 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 40 nu = 0.364056 obj = -21.037964, rho = -0.155711 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 37 nu = 0.320887 obj = -23.510651, rho = -0.248889 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.276681 obj = -26.390055, rho = -0.216979 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 41 nu = 0.249944 obj = -29.651375, rho = -0.195202 nSV = 28, nBSV = 24 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 39 nu = 0.224798 obj = -32.927042, rho = -0.249943 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 59 nu = 0.197609 obj = -36.143788, rho = -0.244495 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 42 nu = 0.175045 obj = -39.339010, rho = -0.159295 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 68 nu = 0.147266 obj = -42.413265, rho = -0.115625 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 86 nu = 0.131414 obj = -45.365140, rho = -0.193281 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 215 nu = 0.112489 obj = -47.003035, rho = -0.227942 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 71 nu = 0.092000 obj = -48.442469, rho = -0.220549 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -7.058702, rho = -0.573099 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 75% (75/100) (classification) Accuracy = 69.5% (695/1000) (classification) * optimization finished, #iter = 46 nu = 0.860000 obj = -8.456041, rho = -0.456011 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 90% (90/100) (classification) Accuracy = 88% (880/1000) (classification) * optimization finished, #iter = 48 nu = 0.821974 obj = -9.959929, rho = -0.390627 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 95% (95/100) (classification) Accuracy = 93.1% (931/1000) (classification) * optimization finished, #iter = 47 nu = 0.781729 obj = -11.589298, rho = -0.303857 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 37 nu = 0.720000 obj = -13.386632, rho = -0.355820 nSV = 73, nBSV = 71 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 54 nu = 0.665125 obj = -15.278632, rho = -0.365368 nSV = 70, nBSV = 63 Total nSV = 70 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 48 nu = 0.596714 obj = -17.392444, rho = -0.332511 nSV = 62, nBSV = 55 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 45 nu = 0.532091 obj = -19.796594, rho = -0.315926 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 46 nu = 0.476952 obj = -22.521104, rho = -0.281852 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 55 nu = 0.421775 obj = -25.670267, rho = -0.270247 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 65 nu = 0.374427 obj = -29.313305, rho = -0.285197 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 31 nu = 0.336498 obj = -33.656073, rho = -0.359657 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 45 nu = 0.300619 obj = -38.563407, rho = -0.333703 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 64 nu = 0.277797 obj = -43.975763, rho = -0.307798 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 71 nu = 0.245862 obj = -50.021977, rho = -0.305565 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 49 nu = 0.215016 obj = -57.279995, rho = -0.253204 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 63 nu = 0.192284 obj = -66.113398, rho = -0.287443 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 90 nu = 0.175454 obj = -75.925790, rho = -0.419350 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.157586 obj = -87.638568, rho = -0.344358 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 73 nu = 0.142869 obj = -100.754886, rho = -0.335879 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 53 nu = 0.906045 obj = -7.049990, rho = 0.104541 nSV = 92, nBSV = 88 Total nSV = 92 Accuracy = 95% (95/100) (classification) Accuracy = 91.2% (912/1000) (classification) * optimization finished, #iter = 44 nu = 0.874311 obj = -8.322203, rho = -0.024299 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 96% (96/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 48 nu = 0.824869 obj = -9.734610, rho = -0.084834 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 42 nu = 0.760284 obj = -11.292679, rho = -0.027080 nSV = 78, nBSV = 75 Total nSV = 78 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 38 nu = 0.692535 obj = -13.073434, rho = 0.030810 nSV = 71, nBSV = 68 Total nSV = 71 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 36 nu = 0.644469 obj = -15.073109, rho = -0.018950 nSV = 66, nBSV = 63 Total nSV = 66 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.581559 obj = -17.235372, rho = 0.026566 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 50 nu = 0.523698 obj = -19.684320, rho = 0.062546 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 42 nu = 0.465533 obj = -22.545399, rho = 0.047619 nSV = 51, nBSV = 43 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 33 nu = 0.415490 obj = -25.923010, rho = 0.099989 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 41 nu = 0.383910 obj = -29.706775, rho = 0.042648 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 31 nu = 0.346923 obj = -33.801809, rho = 0.151871 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 49 nu = 0.317952 obj = -38.125126, rho = 0.249917 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 47 nu = 0.283308 obj = -42.839184, rho = 0.350160 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 91 nu = 0.245408 obj = -47.890596, rho = 0.337309 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 66 nu = 0.215312 obj = -53.827641, rho = 0.411836 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.189295 obj = -60.658647, rho = 0.486085 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 67 nu = 0.176272 obj = -67.647103, rho = 0.633905 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 63 nu = 0.152386 obj = -74.149503, rho = 0.677930 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 76 nu = 0.137928 obj = -80.888358, rho = 0.749417 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -6.507599, rho = 0.051165 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 42 nu = 0.831635 obj = -7.523125, rho = 0.027139 nSV = 84, nBSV = 82 Total nSV = 84 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 41 nu = 0.777041 obj = -8.631700, rho = -0.000411 nSV = 78, nBSV = 76 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 44 nu = 0.705614 obj = -9.770849, rho = 0.010805 nSV = 73, nBSV = 70 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 36 nu = 0.636972 obj = -10.997741, rho = 0.048512 nSV = 65, nBSV = 62 Total nSV = 65 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 33 nu = 0.558736 obj = -12.329487, rho = 0.068773 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 39 nu = 0.497733 obj = -13.816212, rho = 0.056517 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.437674 obj = -15.389543, rho = 0.116157 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 52 nu = 0.378239 obj = -17.177751, rho = 0.116772 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 32 nu = 0.331074 obj = -19.287497, rho = 0.098798 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 67 nu = 0.289703 obj = -21.673855, rho = 0.135711 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 79 nu = 0.257288 obj = -24.446425, rho = 0.069110 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 49 nu = 0.229105 obj = -27.493156, rho = -0.045330 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 49 nu = 0.201657 obj = -30.893728, rho = -0.059159 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.175721 obj = -34.618186, rho = -0.063741 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 67 nu = 0.154287 obj = -39.052895, rho = -0.124240 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 71 nu = 0.135309 obj = -44.249890, rho = -0.113263 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 74 nu = 0.121304 obj = -50.144878, rho = -0.089115 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 74 nu = 0.105348 obj = -56.983256, rho = -0.098136 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.095875 obj = -65.072725, rho = 0.033885 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 51 nu = 0.895676 obj = -6.629674, rho = -0.352541 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 96% (96/100) (classification) Accuracy = 93.6% (936/1000) (classification) * optimization finished, #iter = 46 nu = 0.827785 obj = -7.755641, rho = -0.312210 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 96% (96/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 42 nu = 0.760515 obj = -9.044620, rho = -0.268238 nSV = 78, nBSV = 74 Total nSV = 78 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 42 nu = 0.708406 obj = -10.520566, rho = -0.281120 nSV = 72, nBSV = 68 Total nSV = 72 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 38 nu = 0.651859 obj = -12.162177, rho = -0.222295 nSV = 68, nBSV = 64 Total nSV = 68 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 53 nu = 0.598351 obj = -13.938621, rho = -0.150737 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 32 nu = 0.539329 obj = -15.965111, rho = -0.164392 nSV = 55, nBSV = 52 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 35 nu = 0.484927 obj = -18.279186, rho = -0.147748 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 33 nu = 0.449275 obj = -20.793820, rho = -0.067547 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 28 nu = 0.400543 obj = -23.455862, rho = -0.087956 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 53 nu = 0.356731 obj = -26.285083, rho = -0.063917 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.309712 obj = -29.520944, rho = -0.065565 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 62 nu = 0.277729 obj = -33.107658, rho = -0.064863 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 58 nu = 0.246017 obj = -37.076782, rho = -0.169879 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 77 nu = 0.216452 obj = -41.162868, rho = -0.224808 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 45 nu = 0.188725 obj = -45.749834, rho = -0.217738 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.162528 obj = -51.041949, rho = -0.208000 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 80 nu = 0.142551 obj = -57.142809, rho = -0.136805 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *..* optimization finished, #iter = 200 nu = 0.122214 obj = -64.279443, rho = -0.117214 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 162 nu = 0.110749 obj = -72.376189, rho = -0.180002 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 38 nu = 0.740000 obj = -6.382553, rho = -0.659654 nSV = 75, nBSV = 73 Total nSV = 75 Accuracy = 68% (68/100) (classification) Accuracy = 53.2% (532/1000) (classification) * optimization finished, #iter = 38 nu = 0.740000 obj = -7.777528, rho = -0.566306 nSV = 75, nBSV = 73 Total nSV = 75 Accuracy = 78% (78/100) (classification) Accuracy = 70.7% (707/1000) (classification) * optimization finished, #iter = 38 nu = 0.740000 obj = -9.333296, rho = -0.447355 nSV = 75, nBSV = 73 Total nSV = 75 Accuracy = 90% (90/100) (classification) Accuracy = 87.6% (876/1000) (classification) * optimization finished, #iter = 40 nu = 0.716753 obj = -10.984786, rho = -0.346209 nSV = 73, nBSV = 70 Total nSV = 73 Accuracy = 93% (93/100) (classification) Accuracy = 94.5% (945/1000) (classification) * optimization finished, #iter = 38 nu = 0.657915 obj = -12.854415, rho = -0.351492 nSV = 67, nBSV = 64 Total nSV = 67 Accuracy = 93% (93/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 41 nu = 0.607133 obj = -15.018115, rho = -0.314565 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 94% (94/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 43 nu = 0.556359 obj = -17.534419, rho = -0.286165 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 94% (94/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 33 nu = 0.514784 obj = -20.488167, rho = -0.355433 nSV = 53, nBSV = 49 Total nSV = 53 Accuracy = 94% (94/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 33 nu = 0.474875 obj = -23.836641, rho = -0.344000 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 94% (94/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 33 nu = 0.435010 obj = -27.694529, rho = -0.274953 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 94% (94/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 34 nu = 0.391125 obj = -32.209741, rho = -0.295667 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 94% (94/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 48 nu = 0.354539 obj = -37.585005, rho = -0.250130 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 95% (95/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 83 nu = 0.320298 obj = -44.055447, rho = -0.217497 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 95% (95/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 51 nu = 0.303080 obj = -51.624600, rho = -0.061854 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 57 nu = 0.275740 obj = -60.028439, rho = -0.010084 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 68 nu = 0.258197 obj = -69.515849, rho = -0.025905 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 81 nu = 0.227939 obj = -80.684983, rho = -0.011323 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 78 nu = 0.211570 obj = -93.678660, rho = 0.086755 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 98 nu = 0.190123 obj = -108.592973, rho = 0.058847 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 69 nu = 0.181916 obj = -125.418509, rho = 0.249454 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 47 nu = 0.903211 obj = -6.822962, rho = -0.282620 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 96% (96/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 44 nu = 0.859915 obj = -7.993027, rho = -0.243138 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 97% (97/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 42 nu = 0.802287 obj = -9.271931, rho = -0.252455 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 97% (97/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 45 nu = 0.749452 obj = -10.624711, rho = -0.215834 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 45 nu = 0.670266 obj = -12.099286, rho = -0.239703 nSV = 71, nBSV = 65 Total nSV = 71 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 44 nu = 0.608381 obj = -13.731752, rho = -0.187905 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.538706 obj = -15.546031, rho = -0.169176 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 35 nu = 0.476303 obj = -17.665419, rho = -0.192023 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 54 nu = 0.423856 obj = -20.133947, rho = -0.191583 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 61 nu = 0.373363 obj = -23.005223, rho = -0.201248 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 75 nu = 0.337342 obj = -26.301499, rho = -0.150672 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 55 nu = 0.303148 obj = -30.090420, rho = -0.121425 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 47 nu = 0.274532 obj = -34.401163, rho = -0.135171 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.250475 obj = -39.012528, rho = -0.223222 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 64 nu = 0.221801 obj = -43.989906, rho = -0.245966 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.198416 obj = -49.511947, rho = -0.314101 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 71 nu = 0.175063 obj = -55.604934, rho = -0.483685 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 88 nu = 0.159115 obj = -62.088295, rho = -0.459149 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 80 nu = 0.139809 obj = -68.476005, rho = -0.421740 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *..* optimization finished, #iter = 287 nu = 0.124514 obj = -74.884691, rho = -0.413791 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 47 nu = 0.892235 obj = -6.335192, rho = -0.299359 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 98% (98/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 42 nu = 0.818523 obj = -7.289864, rho = -0.273247 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 40 nu = 0.741760 obj = -8.353430, rho = -0.274568 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 43 nu = 0.677049 obj = -9.537057, rho = -0.239576 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 47 nu = 0.612925 obj = -10.795729, rho = -0.182226 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 59 nu = 0.547952 obj = -12.182567, rho = -0.175116 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 41 nu = 0.488193 obj = -13.693768, rho = -0.228211 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.428954 obj = -15.346115, rho = -0.205600 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 32 nu = 0.380000 obj = -17.245325, rho = -0.285343 nSV = 39, nBSV = 36 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 37 nu = 0.334088 obj = -19.306925, rho = -0.332320 nSV = 35, nBSV = 32 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 33 nu = 0.293244 obj = -21.542411, rho = -0.324602 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 50 nu = 0.261728 obj = -24.003217, rho = -0.396909 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 47 nu = 0.228482 obj = -26.552308, rho = -0.431329 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 90 nu = 0.200822 obj = -29.293687, rho = -0.402427 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 154 nu = 0.175437 obj = -32.039737, rho = -0.352113 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 69 nu = 0.149572 obj = -35.124037, rho = -0.318128 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.132201 obj = -38.348718, rho = -0.276527 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 165 nu = 0.113102 obj = -41.645992, rho = -0.236548 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*..* optimization finished, #iter = 343 nu = 0.096328 obj = -45.033871, rho = -0.204736 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 172 nu = 0.080297 obj = -48.889305, rho = -0.184488 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 47 nu = 0.930163 obj = -7.051132, rho = 0.096202 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 96% (96/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 48 nu = 0.877045 obj = -8.278030, rho = 0.023463 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 96% (96/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 50 nu = 0.804006 obj = -9.681393, rho = 0.047181 nSV = 84, nBSV = 78 Total nSV = 84 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 46 nu = 0.747814 obj = -11.313832, rho = 0.119523 nSV = 77, nBSV = 74 Total nSV = 77 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 37 nu = 0.700000 obj = -13.136828, rho = 0.135802 nSV = 71, nBSV = 69 Total nSV = 71 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 35 nu = 0.640349 obj = -15.153132, rho = 0.090571 nSV = 66, nBSV = 63 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 35 nu = 0.591738 obj = -17.349710, rho = 0.027777 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 96% (96/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 42 nu = 0.527846 obj = -19.772348, rho = 0.033362 nSV = 56, nBSV = 50 Total nSV = 56 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.465049 obj = -22.631715, rho = 0.010497 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 96% (96/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 42 nu = 0.420207 obj = -25.915331, rho = -0.012474 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 96% (96/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 57 nu = 0.376621 obj = -29.682544, rho = -0.028438 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 73 nu = 0.341299 obj = -34.010061, rho = 0.024303 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 61 nu = 0.303340 obj = -38.972234, rho = 0.038924 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 46 nu = 0.273613 obj = -44.863089, rho = 0.093396 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 96% (96/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 65 nu = 0.244010 obj = -51.677876, rho = 0.105805 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 48 nu = 0.217024 obj = -59.901236, rho = 0.082163 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 39 nu = 0.200000 obj = -69.896423, rho = 0.024108 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 62 nu = 0.185008 obj = -80.612620, rho = -0.065307 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) ..* optimization finished, #iter = 265 nu = 0.165073 obj = -93.159009, rho = -0.086728 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*...* optimization finished, #iter = 449 nu = 0.146011 obj = -108.602059, rho = -0.106120 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -6.902525, rho = 0.068974 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 87% (87/100) (classification) Accuracy = 86.2% (862/1000) (classification) * optimization finished, #iter = 45 nu = 0.862860 obj = -8.138442, rho = -0.103204 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 92% (92/100) (classification) Accuracy = 94% (940/1000) (classification) * optimization finished, #iter = 43 nu = 0.829756 obj = -9.439038, rho = -0.168198 nSV = 85, nBSV = 81 Total nSV = 85 Accuracy = 95% (95/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 54 nu = 0.750635 obj = -10.832416, rho = -0.208328 nSV = 78, nBSV = 73 Total nSV = 78 Accuracy = 95% (95/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 42 nu = 0.684045 obj = -12.388446, rho = -0.158567 nSV = 70, nBSV = 67 Total nSV = 70 Accuracy = 96% (96/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 69 nu = 0.620564 obj = -14.067521, rho = -0.124514 nSV = 66, nBSV = 59 Total nSV = 66 Accuracy = 96% (96/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 42 nu = 0.548130 obj = -15.986091, rho = -0.151765 nSV = 57, nBSV = 51 Total nSV = 57 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 68 nu = 0.481132 obj = -18.245710, rho = -0.143284 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 95% (95/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.440205 obj = -20.785863, rho = -0.096361 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 77 nu = 0.398530 obj = -23.521764, rho = -0.060689 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.353061 obj = -26.572985, rho = -0.110594 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 47 nu = 0.313539 obj = -29.969291, rho = -0.141868 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 94 nu = 0.272671 obj = -33.912830, rho = -0.197348 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.247686 obj = -38.430750, rho = -0.307122 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.216021 obj = -43.405664, rho = -0.300147 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 93 nu = 0.192188 obj = -49.338773, rho = -0.312679 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *..* optimization finished, #iter = 200 nu = 0.176795 obj = -55.477387, rho = -0.274667 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *...* optimization finished, #iter = 368 nu = 0.153524 obj = -62.145432, rho = -0.283795 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.139695 obj = -69.247851, rho = -0.350733 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 86 nu = 0.126135 obj = -76.339141, rho = -0.444012 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.900000 obj = -7.130757, rho = 0.235845 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 86% (86/100) (classification) Accuracy = 82.3% (823/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -8.433242, rho = 0.026257 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 97% (97/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 43 nu = 0.842783 obj = -9.791423, rho = -0.027563 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 43 nu = 0.772501 obj = -11.301202, rho = -0.063985 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 38 nu = 0.715292 obj = -12.990475, rho = -0.063136 nSV = 72, nBSV = 70 Total nSV = 72 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 55 nu = 0.653821 obj = -14.762999, rho = -0.019175 nSV = 68, nBSV = 62 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 48 nu = 0.583246 obj = -16.673808, rho = 0.068006 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 48 nu = 0.518583 obj = -18.777874, rho = 0.049288 nSV = 56, nBSV = 50 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 67 nu = 0.467139 obj = -21.065142, rho = -0.016303 nSV = 50, nBSV = 43 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 54 nu = 0.401526 obj = -23.660944, rho = -0.017799 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 173 nu = 0.352831 obj = -26.727944, rho = -0.009881 nSV = 40, nBSV = 31 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 31 nu = 0.308420 obj = -30.399965, rho = 0.002971 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 50 nu = 0.283473 obj = -34.344889, rho = -0.096589 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 58 nu = 0.250609 obj = -38.743145, rho = -0.108823 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.223620 obj = -43.413129, rho = -0.148440 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 79 nu = 0.196834 obj = -48.742114, rho = -0.102067 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 136 nu = 0.180699 obj = -54.016567, rho = -0.088265 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 246 nu = 0.153960 obj = -59.261468, rho = -0.051031 nSV = 20, nBSV = 9 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 147 nu = 0.130392 obj = -65.730413, rho = -0.042707 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 161 nu = 0.112758 obj = -73.143875, rho = -0.041289 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.940818 obj = -6.906919, rho = -0.073019 nSV = 96, nBSV = 94 Total nSV = 96 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 44 nu = 0.868571 obj = -8.063480, rho = -0.059659 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 42 nu = 0.799560 obj = -9.368518, rho = -0.038009 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 38 nu = 0.725336 obj = -10.887559, rho = -0.033769 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 39 nu = 0.668347 obj = -12.622311, rho = 0.013564 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 36 nu = 0.623103 obj = -14.541210, rho = 0.105872 nSV = 64, nBSV = 61 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 43 nu = 0.574407 obj = -16.542590, rho = 0.090680 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 33 nu = 0.510265 obj = -18.682902, rho = 0.079776 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 36 nu = 0.460000 obj = -20.986821, rho = 0.126829 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 75 nu = 0.407834 obj = -23.453576, rho = 0.084190 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.352952 obj = -26.281780, rho = 0.105726 nSV = 38, nBSV = 30 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 62 nu = 0.307181 obj = -29.697510, rho = 0.092396 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 45 nu = 0.268158 obj = -33.791886, rho = 0.085390 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.242870 obj = -38.377469, rho = 0.137528 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.216656 obj = -43.489447, rho = 0.131090 nSV = 27, nBSV = 16 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.187278 obj = -49.618926, rho = 0.125973 nSV = 25, nBSV = 15 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 69 nu = 0.166701 obj = -57.167196, rho = 0.100916 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 67 nu = 0.148871 obj = -66.056419, rho = 0.066999 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 88 nu = 0.140767 obj = -76.013378, rho = -0.070459 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 96 nu = 0.127576 obj = -86.616111, rho = -0.101950 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -6.674714, rho = -0.554395 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 75% (75/100) (classification) Accuracy = 70.9% (709/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -7.972331, rho = -0.432176 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 92% (92/100) (classification) Accuracy = 89.6% (896/1000) (classification) * optimization finished, #iter = 43 nu = 0.801737 obj = -9.304427, rho = -0.343105 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 98% (98/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 45 nu = 0.748804 obj = -10.669955, rho = -0.267008 nSV = 78, nBSV = 73 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 50 nu = 0.680488 obj = -12.175002, rho = -0.266910 nSV = 71, nBSV = 66 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 51 nu = 0.609748 obj = -13.807323, rho = -0.267391 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.553679 obj = -15.552605, rho = -0.235373 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 51 nu = 0.484031 obj = -17.469529, rho = -0.242472 nSV = 51, nBSV = 44 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 32 nu = 0.426020 obj = -19.750877, rho = -0.208224 nSV = 45, nBSV = 41 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 40 nu = 0.379364 obj = -22.202179, rho = -0.216567 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 38 nu = 0.338773 obj = -24.944483, rho = -0.173749 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 48 nu = 0.297667 obj = -27.933109, rho = -0.134796 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 53 nu = 0.260912 obj = -31.289816, rho = -0.124555 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 68 nu = 0.230807 obj = -34.909838, rho = -0.229560 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 70 nu = 0.197513 obj = -39.191360, rho = -0.226088 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 74 nu = 0.173709 obj = -44.343475, rho = -0.259856 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 69 nu = 0.154156 obj = -50.223393, rho = -0.302010 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 57 nu = 0.137915 obj = -56.921580, rho = -0.285307 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 65 nu = 0.121555 obj = -64.336250, rho = -0.346246 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *..* optimization finished, #iter = 207 nu = 0.110724 obj = -72.212188, rho = -0.540326 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -7.290433, rho = -0.491539 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 86% (86/100) (classification) Accuracy = 86.6% (866/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -8.637150, rho = -0.401442 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 96% (96/100) (classification) Accuracy = 93.5% (935/1000) (classification) * optimization finished, #iter = 49 nu = 0.856848 obj = -10.073175, rho = -0.323952 nSV = 87, nBSV = 84 Total nSV = 87 Accuracy = 95% (95/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 53 nu = 0.792644 obj = -11.684756, rho = -0.271121 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 96% (96/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 44 nu = 0.723357 obj = -13.464364, rho = -0.269905 nSV = 75, nBSV = 70 Total nSV = 75 Accuracy = 96% (96/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 41 nu = 0.650322 obj = -15.514721, rho = -0.245131 nSV = 67, nBSV = 61 Total nSV = 67 Accuracy = 96% (96/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 43 nu = 0.587206 obj = -17.937974, rho = -0.218609 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 31 nu = 0.535235 obj = -20.756656, rho = -0.207765 nSV = 55, nBSV = 52 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 33 nu = 0.495277 obj = -23.872175, rho = -0.149144 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 47 nu = 0.447421 obj = -27.323246, rho = -0.184260 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 60 nu = 0.406580 obj = -31.103667, rho = -0.221783 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 58 nu = 0.359856 obj = -35.338450, rho = -0.225376 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.322674 obj = -40.157506, rho = -0.299973 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 36 nu = 0.289718 obj = -45.629632, rho = -0.339991 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 39 nu = 0.256099 obj = -51.683570, rho = -0.364899 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 70 nu = 0.224422 obj = -58.925142, rho = -0.337349 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 157 nu = 0.204351 obj = -67.293016, rho = -0.303562 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 86 nu = 0.184303 obj = -76.367932, rho = -0.287019 nSV = 22, nBSV = 17 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 59 nu = 0.171535 obj = -85.740636, rho = -0.325976 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 179 nu = 0.152853 obj = -94.121545, rho = -0.337805 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 48 nu = 0.930789 obj = -7.228420, rho = 0.030117 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 89% (89/100) (classification) Accuracy = 91.1% (911/1000) (classification) * optimization finished, #iter = 47 nu = 0.892612 obj = -8.540468, rho = 0.043489 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 95% (95/100) (classification) Accuracy = 93.3% (933/1000) (classification) * optimization finished, #iter = 47 nu = 0.840000 obj = -10.011349, rho = 0.021903 nSV = 85, nBSV = 81 Total nSV = 85 Accuracy = 97% (97/100) (classification) Accuracy = 95.1% (951/1000) (classification) * optimization finished, #iter = 57 nu = 0.782938 obj = -11.623912, rho = -0.018548 nSV = 80, nBSV = 75 Total nSV = 80 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 37 nu = 0.719823 obj = -13.445858, rho = -0.120815 nSV = 72, nBSV = 70 Total nSV = 72 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 41 nu = 0.668756 obj = -15.459108, rho = -0.253720 nSV = 68, nBSV = 63 Total nSV = 68 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 40 nu = 0.603669 obj = -17.624340, rho = -0.192230 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 47 nu = 0.557449 obj = -19.986272, rho = -0.184279 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 78 nu = 0.490689 obj = -22.398201, rho = -0.168696 nSV = 54, nBSV = 46 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 37 nu = 0.436815 obj = -25.117216, rho = -0.199223 nSV = 45, nBSV = 41 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 78 nu = 0.385650 obj = -28.001764, rho = -0.186310 nSV = 44, nBSV = 36 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 64 nu = 0.336110 obj = -31.198896, rho = -0.184209 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 40 nu = 0.295838 obj = -34.845018, rho = -0.081490 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 49 nu = 0.259510 obj = -38.630649, rho = -0.074225 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 55 nu = 0.227505 obj = -42.910874, rho = -0.084505 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 55 nu = 0.193689 obj = -47.665017, rho = -0.073482 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 91 nu = 0.174937 obj = -52.939142, rho = 0.015548 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 87 nu = 0.152178 obj = -58.057684, rho = 0.068047 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *......* optimization finished, #iter = 682 nu = 0.129896 obj = -63.761117, rho = 0.034705 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 289 nu = 0.111462 obj = -70.595332, rho = 0.050910 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 51 nu = 0.917692 obj = -6.708090, rho = -0.160982 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 96% (96/100) (classification) Accuracy = 94.3% (943/1000) (classification) * optimization finished, #iter = 48 nu = 0.841607 obj = -7.817815, rho = -0.122797 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 96% (96/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 44 nu = 0.793440 obj = -9.030726, rho = -0.138606 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 97% (97/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 49 nu = 0.727057 obj = -10.326410, rho = -0.121371 nSV = 75, nBSV = 70 Total nSV = 75 Accuracy = 97% (97/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 48 nu = 0.653347 obj = -11.768129, rho = -0.127593 nSV = 68, nBSV = 63 Total nSV = 68 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 44 nu = 0.581602 obj = -13.374633, rho = -0.120482 nSV = 62, nBSV = 55 Total nSV = 62 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 29 nu = 0.520000 obj = -15.242994, rho = -0.146480 nSV = 54, nBSV = 51 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 51 nu = 0.466563 obj = -17.332895, rho = -0.179396 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 54 nu = 0.412528 obj = -19.739714, rho = -0.171421 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 51 nu = 0.369136 obj = -22.550824, rho = -0.165717 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 38 nu = 0.328289 obj = -25.829020, rho = -0.162669 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 40 nu = 0.304575 obj = -29.461672, rho = -0.175877 nSV = 33, nBSV = 29 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 61 nu = 0.274320 obj = -33.179454, rho = -0.188197 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 98 nu = 0.240421 obj = -37.303411, rho = -0.188326 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *..* optimization finished, #iter = 291 nu = 0.207868 obj = -42.260173, rho = -0.207087 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 154 nu = 0.181606 obj = -48.366230, rho = -0.174872 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 64 nu = 0.161051 obj = -55.766449, rho = -0.166653 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.148902 obj = -64.554942, rho = -0.207787 nSV = 18, nBSV = 13 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 72 nu = 0.139731 obj = -73.684755, rho = -0.284785 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 66 nu = 0.125956 obj = -83.158235, rho = -0.240418 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.932192 obj = -7.002889, rho = -0.149008 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 96% (96/100) (classification) Accuracy = 93.9% (939/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -8.211921, rho = -0.192415 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 41 nu = 0.818874 obj = -9.543610, rho = -0.174833 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 49 nu = 0.752850 obj = -11.027428, rho = -0.107501 nSV = 78, nBSV = 73 Total nSV = 78 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 48 nu = 0.675278 obj = -12.734691, rho = -0.102636 nSV = 71, nBSV = 66 Total nSV = 71 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.637058 obj = -14.627876, rho = -0.076115 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 39 nu = 0.575638 obj = -16.657781, rho = -0.091050 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 62 nu = 0.515245 obj = -18.844254, rho = -0.070566 nSV = 55, nBSV = 47 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 77 nu = 0.453219 obj = -21.343778, rho = -0.079813 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 71 nu = 0.401754 obj = -24.249717, rho = -0.143609 nSV = 46, nBSV = 36 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 38 nu = 0.357950 obj = -27.641089, rho = -0.133520 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 81 nu = 0.325775 obj = -31.327861, rho = -0.037626 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 98 nu = 0.289261 obj = -35.442690, rho = -0.011713 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 70 nu = 0.254104 obj = -40.140207, rho = 0.012533 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 97 nu = 0.231286 obj = -45.505067, rho = 0.108900 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.200313 obj = -51.364347, rho = 0.106641 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 75 nu = 0.177803 obj = -58.410443, rho = 0.125081 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 82 nu = 0.162657 obj = -66.012179, rho = 0.103802 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.150834 obj = -73.392629, rho = 0.149363 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.127280 obj = -80.959680, rho = 0.166956 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 47 nu = 0.928225 obj = -6.812597, rho = -0.176529 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 97% (97/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 46 nu = 0.861115 obj = -7.922670, rho = -0.182385 nSV = 88, nBSV = 85 Total nSV = 88 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 46 nu = 0.797730 obj = -9.166567, rho = -0.151710 nSV = 82, nBSV = 78 Total nSV = 82 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 40 nu = 0.732921 obj = -10.537676, rho = -0.170350 nSV = 74, nBSV = 71 Total nSV = 74 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.666850 obj = -12.061511, rho = -0.184190 nSV = 69, nBSV = 64 Total nSV = 69 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 54 nu = 0.589939 obj = -13.782648, rho = -0.163713 nSV = 64, nBSV = 58 Total nSV = 64 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 31 nu = 0.529561 obj = -15.804710, rho = -0.145483 nSV = 54, nBSV = 51 Total nSV = 54 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 39 nu = 0.483491 obj = -18.096213, rho = -0.075524 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 97% (97/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 82 nu = 0.435234 obj = -20.611376, rho = -0.062565 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 97% (97/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 33 nu = 0.388889 obj = -23.469055, rho = -0.040315 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 97% (97/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 36 nu = 0.353388 obj = -26.699296, rho = -0.081805 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 97% (97/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 34 nu = 0.313189 obj = -30.128157, rho = -0.061789 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 63 nu = 0.279607 obj = -33.989604, rho = -0.068652 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.244843 obj = -38.350381, rho = -0.035677 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 97% (97/100) (classification) Accuracy = 99.3% (993/1000) (classification) * optimization finished, #iter = 43 nu = 0.219085 obj = -43.434353, rho = -0.009678 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 99.2% (992/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.189746 obj = -49.137615, rho = 0.013717 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 73 nu = 0.164431 obj = -56.333433, rho = 0.015479 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 42 nu = 0.145917 obj = -65.305224, rho = 0.028359 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 46 nu = 0.133400 obj = -75.964405, rho = 0.031374 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 31 nu = 0.125900 obj = -87.850224, rho = 0.006560 nSV = 15, nBSV = 10 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -7.465153, rho = -0.425200 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 72% (72/100) (classification) Accuracy = 77.3% (773/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -8.976226, rho = -0.267547 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 92% (92/100) (classification) Accuracy = 94% (940/1000) (classification) * optimization finished, #iter = 54 nu = 0.876514 obj = -10.610921, rho = -0.149509 nSV = 89, nBSV = 85 Total nSV = 89 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 54 nu = 0.812309 obj = -12.439241, rho = -0.095467 nSV = 85, nBSV = 80 Total nSV = 85 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 49 nu = 0.763220 obj = -14.493656, rho = -0.067095 nSV = 79, nBSV = 75 Total nSV = 79 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 39 nu = 0.693326 obj = -16.806040, rho = -0.030264 nSV = 72, nBSV = 68 Total nSV = 72 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 37 nu = 0.640000 obj = -19.501848, rho = 0.009689 nSV = 66, nBSV = 63 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 47 nu = 0.588307 obj = -22.340885, rho = 0.074390 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 61 nu = 0.529813 obj = -25.620067, rho = 0.089072 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.467912 obj = -29.415120, rho = 0.098473 nSV = 52, nBSV = 42 Total nSV = 52 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 58 nu = 0.425056 obj = -33.944463, rho = 0.104706 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 42 nu = 0.393059 obj = -39.139471, rho = 0.161547 nSV = 41, nBSV = 37 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 65 nu = 0.353981 obj = -44.722874, rho = 0.135880 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.325579 obj = -50.925647, rho = 0.147180 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 99 nu = 0.285383 obj = -57.720482, rho = 0.117793 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 64 nu = 0.259440 obj = -65.361175, rho = 0.097277 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 86 nu = 0.229600 obj = -73.663690, rho = 0.065777 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 93 nu = 0.211084 obj = -82.181334, rho = -0.025708 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.184370 obj = -90.834434, rho = 0.098995 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.158014 obj = -100.483420, rho = 0.092118 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 49 nu = 0.929234 obj = -6.957383, rho = -0.116633 nSV = 94, nBSV = 91 Total nSV = 94 Accuracy = 97% (97/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 47 nu = 0.882696 obj = -8.122288, rho = -0.140330 nSV = 90, nBSV = 87 Total nSV = 90 Accuracy = 98% (98/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 41 nu = 0.819885 obj = -9.379233, rho = -0.164623 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 97% (97/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 44 nu = 0.743485 obj = -10.792339, rho = -0.109083 nSV = 77, nBSV = 72 Total nSV = 77 Accuracy = 97% (97/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 42 nu = 0.683030 obj = -12.356922, rho = -0.113845 nSV = 70, nBSV = 66 Total nSV = 70 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 53 nu = 0.606554 obj = -14.118441, rho = -0.176223 nSV = 64, nBSV = 56 Total nSV = 64 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 34 nu = 0.542368 obj = -16.202404, rho = -0.183343 nSV = 56, nBSV = 53 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 42 nu = 0.500733 obj = -18.457714, rho = -0.167802 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 55 nu = 0.454301 obj = -20.859147, rho = -0.123637 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 66 nu = 0.398958 obj = -23.490654, rho = -0.061471 nSV = 44, nBSV = 36 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 43 nu = 0.352851 obj = -26.529739, rho = -0.100612 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 48 nu = 0.314038 obj = -29.899757, rho = -0.107024 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 82 nu = 0.275154 obj = -33.678765, rho = -0.095809 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 86 nu = 0.243263 obj = -38.103184, rho = -0.029025 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 81 nu = 0.213145 obj = -43.277886, rho = -0.035356 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.193110 obj = -49.194816, rho = -0.006798 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 163 nu = 0.169864 obj = -55.725684, rho = 0.014297 nSV = 23, nBSV = 12 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.148755 obj = -63.581746, rho = -0.008073 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.133027 obj = -72.967906, rho = -0.032729 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.120000 obj = -83.734727, rho = -0.093339 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.920000 obj = -6.733053, rho = -0.283071 nSV = 92, nBSV = 92 Total nSV = 92 Accuracy = 98% (98/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 46 nu = 0.868973 obj = -7.791938, rho = -0.218757 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 45 nu = 0.794318 obj = -8.934689, rho = -0.213668 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 48 nu = 0.728141 obj = -10.196791, rho = -0.207539 nSV = 74, nBSV = 69 Total nSV = 74 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 50 nu = 0.658407 obj = -11.584302, rho = -0.230747 nSV = 69, nBSV = 62 Total nSV = 69 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 38 nu = 0.582438 obj = -13.109475, rho = -0.175860 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 47 nu = 0.541053 obj = -14.659861, rho = -0.269797 nSV = 57, nBSV = 50 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.478621 obj = -16.201012, rho = -0.227195 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 54 nu = 0.413360 obj = -17.782938, rho = -0.205153 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 68 nu = 0.350031 obj = -19.593096, rho = -0.223953 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 26 nu = 0.305659 obj = -21.663178, rho = -0.265057 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.263259 obj = -23.935230, rho = -0.280093 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 80 nu = 0.227773 obj = -26.529934, rho = -0.358001 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 59 nu = 0.194283 obj = -29.578393, rho = -0.368651 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 75 nu = 0.174595 obj = -32.875802, rho = -0.338106 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 61 nu = 0.152716 obj = -36.351984, rho = -0.318258 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 57 nu = 0.134340 obj = -40.160737, rho = -0.338520 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 155 nu = 0.118646 obj = -43.630364, rho = -0.333934 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.101062 obj = -47.149669, rho = -0.319871 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *..* optimization finished, #iter = 201 nu = 0.084118 obj = -51.064934, rho = -0.337653 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -6.926577, rho = -0.018375 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 98% (98/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 46 nu = 0.875356 obj = -8.070755, rho = -0.047201 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 98% (98/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 43 nu = 0.811851 obj = -9.353481, rho = -0.028480 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 43 nu = 0.755452 obj = -10.757570, rho = -0.022827 nSV = 77, nBSV = 74 Total nSV = 77 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 40 nu = 0.678094 obj = -12.281542, rho = -0.029259 nSV = 69, nBSV = 65 Total nSV = 69 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 44 nu = 0.608348 obj = -14.018293, rho = -0.049889 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 31 nu = 0.556975 obj = -15.905712, rho = -0.073522 nSV = 56, nBSV = 54 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.493376 obj = -17.922282, rho = -0.064538 nSV = 53, nBSV = 46 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 52 nu = 0.437061 obj = -20.229181, rho = -0.036169 nSV = 47, nBSV = 39 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 67 nu = 0.389832 obj = -22.847969, rho = -0.048582 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.342939 obj = -25.774732, rho = -0.024146 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 87 nu = 0.305569 obj = -28.975972, rho = -0.021745 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 37 nu = 0.267535 obj = -32.678481, rho = -0.049117 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 162 nu = 0.236277 obj = -36.790313, rho = -0.113133 nSV = 30, nBSV = 20 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.204237 obj = -41.774238, rho = -0.118210 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.185515 obj = -47.637869, rho = -0.143707 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 196 nu = 0.166038 obj = -54.043697, rho = -0.173161 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*......* optimization finished, #iter = 804 nu = 0.144005 obj = -61.570593, rho = -0.183010 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 84 nu = 0.127685 obj = -70.767663, rho = -0.185740 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 84 nu = 0.116482 obj = -81.239856, rho = -0.223399 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 47 nu = 0.886165 obj = -6.616672, rho = -0.267985 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 97% (97/100) (classification) Accuracy = 94.6% (946/1000) (classification) * optimization finished, #iter = 43 nu = 0.838052 obj = -7.735831, rho = -0.196611 nSV = 84, nBSV = 82 Total nSV = 84 Accuracy = 97% (97/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 45 nu = 0.778075 obj = -8.945664, rho = -0.150574 nSV = 80, nBSV = 76 Total nSV = 80 Accuracy = 98% (98/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 46 nu = 0.713863 obj = -10.288555, rho = -0.135006 nSV = 73, nBSV = 70 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 43 nu = 0.652606 obj = -11.720139, rho = -0.051365 nSV = 68, nBSV = 63 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 43 nu = 0.587099 obj = -13.309124, rho = -0.096771 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 67 nu = 0.519931 obj = -15.079011, rho = -0.081968 nSV = 55, nBSV = 47 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 48 nu = 0.463219 obj = -17.138946, rho = -0.101935 nSV = 51, nBSV = 44 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 48 nu = 0.423201 obj = -19.361256, rho = -0.131210 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 86 nu = 0.371942 obj = -21.715297, rho = -0.151458 nSV = 42, nBSV = 34 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 62 nu = 0.331959 obj = -24.332034, rho = -0.204918 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 58 nu = 0.286143 obj = -27.297293, rho = -0.198030 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 85 nu = 0.251707 obj = -30.802183, rho = -0.212239 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.220449 obj = -34.830556, rho = -0.240789 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 45 nu = 0.199990 obj = -39.463422, rho = -0.263655 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.177966 obj = -44.262218, rho = -0.351917 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 74 nu = 0.155906 obj = -49.786599, rho = -0.391528 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.133297 obj = -56.406924, rho = -0.386161 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 75 nu = 0.118040 obj = -64.690975, rho = -0.388991 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 160 nu = 0.105575 obj = -74.152425, rho = -0.381177 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.895708 obj = -6.780756, rho = -0.502142 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 93% (93/100) (classification) Accuracy = 91.5% (915/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -7.973746, rho = -0.442109 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 95% (95/100) (classification) Accuracy = 94.2% (942/1000) (classification) * optimization finished, #iter = 42 nu = 0.787193 obj = -9.318973, rho = -0.413671 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 98% (98/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 39 nu = 0.733201 obj = -10.822730, rho = -0.343854 nSV = 74, nBSV = 71 Total nSV = 74 Accuracy = 98% (98/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 40 nu = 0.681434 obj = -12.439635, rho = -0.392217 nSV = 70, nBSV = 67 Total nSV = 70 Accuracy = 98% (98/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 44 nu = 0.604070 obj = -14.229477, rho = -0.378284 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 68 nu = 0.547154 obj = -16.301757, rho = -0.344798 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 37 nu = 0.485127 obj = -18.720213, rho = -0.310063 nSV = 52, nBSV = 45 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 96 nu = 0.434928 obj = -21.596081, rho = -0.305019 nSV = 47, nBSV = 40 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 53 nu = 0.395938 obj = -25.022982, rho = -0.310755 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 46 nu = 0.367289 obj = -28.734537, rho = -0.269090 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 54 nu = 0.329031 obj = -32.813384, rho = -0.269744 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.297280 obj = -37.553063, rho = -0.289993 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 36 nu = 0.267664 obj = -42.897543, rho = -0.304539 nSV = 28, nBSV = 24 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 58 nu = 0.240052 obj = -48.995963, rho = -0.240977 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 69 nu = 0.217743 obj = -55.664636, rho = -0.198501 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 49 nu = 0.195504 obj = -62.967561, rho = -0.253034 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 63 nu = 0.177189 obj = -70.685147, rho = -0.338552 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 54 nu = 0.156641 obj = -78.948127, rho = -0.294574 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 62 nu = 0.138314 obj = -87.559358, rho = -0.366443 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 49 nu = 0.978031 obj = -7.309526, rho = -0.184316 nSV = 98, nBSV = 96 Total nSV = 98 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 47 nu = 0.922223 obj = -8.543874, rho = -0.173922 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 46 nu = 0.879602 obj = -9.854675, rho = -0.085768 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.812442 obj = -11.202782, rho = -0.097436 nSV = 82, nBSV = 79 Total nSV = 82 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 42 nu = 0.738953 obj = -12.569134, rho = -0.075813 nSV = 75, nBSV = 71 Total nSV = 75 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 47 nu = 0.652080 obj = -13.987156, rho = -0.092569 nSV = 67, nBSV = 63 Total nSV = 67 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 54 nu = 0.569305 obj = -15.527334, rho = -0.088857 nSV = 62, nBSV = 54 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 42 nu = 0.491431 obj = -17.242547, rho = -0.108910 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 39 nu = 0.430706 obj = -19.208560, rho = -0.108584 nSV = 45, nBSV = 41 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 27 nu = 0.378615 obj = -21.368182, rho = -0.060290 nSV = 39, nBSV = 36 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 51 nu = 0.327247 obj = -23.732404, rho = -0.089972 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 61 nu = 0.289705 obj = -26.344892, rho = -0.073755 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 55 nu = 0.251296 obj = -29.185194, rho = -0.114647 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 82 nu = 0.217433 obj = -32.317963, rho = -0.103850 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 72 nu = 0.187755 obj = -35.934722, rho = -0.080112 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 74 nu = 0.162248 obj = -40.113173, rho = -0.086875 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 55 nu = 0.143076 obj = -44.914828, rho = -0.155826 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 95 nu = 0.127359 obj = -50.093690, rho = -0.197953 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..*.* optimization finished, #iter = 311 nu = 0.114931 obj = -55.262507, rho = -0.252137 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 206 nu = 0.096844 obj = -60.549383, rho = -0.260676 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -6.823223, rho = -0.005019 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 93% (93/100) (classification) Accuracy = 91.2% (912/1000) (classification) * optimization finished, #iter = 46 nu = 0.859443 obj = -7.994014, rho = 0.002862 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 93% (93/100) (classification) Accuracy = 94% (940/1000) (classification) * optimization finished, #iter = 51 nu = 0.800000 obj = -9.295109, rho = 0.035129 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 94% (94/100) (classification) Accuracy = 95.1% (951/1000) (classification) * optimization finished, #iter = 48 nu = 0.723097 obj = -10.756936, rho = 0.006870 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 94% (94/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 58 nu = 0.665132 obj = -12.432092, rho = 0.035300 nSV = 71, nBSV = 63 Total nSV = 71 Accuracy = 95% (95/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 55 nu = 0.610358 obj = -14.313362, rho = 0.024506 nSV = 63, nBSV = 57 Total nSV = 63 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 43 nu = 0.554176 obj = -16.391772, rho = 0.073698 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 96% (96/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 73 nu = 0.497796 obj = -18.724317, rho = 0.134573 nSV = 54, nBSV = 45 Total nSV = 54 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 69 nu = 0.446137 obj = -21.442635, rho = 0.142580 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 64 nu = 0.401461 obj = -24.455039, rho = 0.176621 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 46 nu = 0.356957 obj = -27.946102, rho = 0.185546 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 185 nu = 0.314144 obj = -32.127256, rho = 0.201519 nSV = 37, nBSV = 28 Total nSV = 37 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 187 nu = 0.284989 obj = -37.135082, rho = 0.243315 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 98 nu = 0.258100 obj = -42.900672, rho = 0.208118 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 82 nu = 0.233044 obj = -49.498523, rho = 0.211458 nSV = 28, nBSV = 18 Total nSV = 28 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 48 nu = 0.205605 obj = -57.573840, rho = 0.198734 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 70 nu = 0.194278 obj = -67.139932, rho = 0.337157 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 166 nu = 0.175738 obj = -77.447674, rho = 0.409412 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 118 nu = 0.160928 obj = -89.534057, rho = 0.493400 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 139 nu = 0.145055 obj = -103.077370, rho = 0.577384 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 46 nu = 0.895641 obj = -6.592452, rho = 0.020472 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 94% (94/100) (classification) Accuracy = 94.8% (948/1000) (classification) * optimization finished, #iter = 50 nu = 0.833219 obj = -7.670141, rho = -0.032707 nSV = 84, nBSV = 82 Total nSV = 84 Accuracy = 96% (96/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 44 nu = 0.773737 obj = -8.887641, rho = -0.090719 nSV = 79, nBSV = 75 Total nSV = 79 Accuracy = 95% (95/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 38 nu = 0.706007 obj = -10.232445, rho = -0.028847 nSV = 72, nBSV = 68 Total nSV = 72 Accuracy = 95% (95/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 34 nu = 0.640000 obj = -11.753138, rho = -0.014189 nSV = 65, nBSV = 63 Total nSV = 65 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 32 nu = 0.580135 obj = -13.462660, rho = -0.080296 nSV = 60, nBSV = 57 Total nSV = 60 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.520917 obj = -15.345992, rho = -0.116317 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 43 nu = 0.466575 obj = -17.556746, rho = -0.163723 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 54 nu = 0.421411 obj = -19.986502, rho = -0.141915 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 96% (96/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 41 nu = 0.370647 obj = -22.818174, rho = -0.141276 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 96% (96/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 34 nu = 0.331989 obj = -26.196907, rho = -0.145569 nSV = 35, nBSV = 31 Total nSV = 35 Accuracy = 96% (96/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 30 nu = 0.299896 obj = -30.048591, rho = -0.202207 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 37 nu = 0.268781 obj = -34.505657, rho = -0.214844 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 34 nu = 0.244256 obj = -39.608234, rho = -0.161060 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 84 nu = 0.218142 obj = -45.297123, rho = -0.183645 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 65 nu = 0.191443 obj = -52.298850, rho = -0.177368 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 96 nu = 0.169896 obj = -60.915376, rho = -0.144115 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.153926 obj = -71.596654, rho = -0.084422 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 91 nu = 0.141826 obj = -84.459240, rho = -0.027221 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 96% (96/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 58 nu = 0.134260 obj = -99.235228, rho = 0.059077 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.908092 obj = -6.869625, rho = -0.086410 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 99% (99/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -8.047722, rho = -0.133801 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 40 nu = 0.800000 obj = -9.344897, rho = -0.113341 nSV = 80, nBSV = 80 Total nSV = 80 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 42 nu = 0.757065 obj = -10.731070, rho = -0.126206 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.688510 obj = -12.183447, rho = -0.111395 nSV = 72, nBSV = 66 Total nSV = 72 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 68 nu = 0.608914 obj = -13.768456, rho = -0.112846 nSV = 65, nBSV = 59 Total nSV = 65 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 37 nu = 0.542859 obj = -15.602452, rho = -0.142828 nSV = 56, nBSV = 53 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 42 nu = 0.484455 obj = -17.584368, rho = -0.162663 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 37 nu = 0.433046 obj = -19.804420, rho = -0.134872 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 28 nu = 0.387221 obj = -22.209033, rho = -0.127288 nSV = 41, nBSV = 37 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 52 nu = 0.348946 obj = -24.526389, rho = -0.102898 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 72 nu = 0.304258 obj = -26.949149, rho = -0.105575 nSV = 35, nBSV = 26 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 51 nu = 0.261998 obj = -29.587456, rho = -0.122551 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 80 nu = 0.227866 obj = -32.257768, rho = -0.142591 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 165 nu = 0.195558 obj = -35.042516, rho = -0.140998 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 79 nu = 0.165144 obj = -38.240834, rho = -0.142417 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 87 nu = 0.148934 obj = -41.273020, rho = -0.196044 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 80 nu = 0.127927 obj = -43.608297, rho = -0.230130 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 161 nu = 0.105854 obj = -45.514797, rho = -0.255159 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 181 nu = 0.085766 obj = -47.684750, rho = -0.246316 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 54 nu = 0.941794 obj = -7.045383, rho = -0.168016 nSV = 96, nBSV = 92 Total nSV = 96 Accuracy = 95% (95/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -8.257314, rho = -0.187993 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 97% (97/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 44 nu = 0.836493 obj = -9.562779, rho = -0.227615 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 98% (98/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 63 nu = 0.769585 obj = -10.962810, rho = -0.187664 nSV = 79, nBSV = 73 Total nSV = 79 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 56 nu = 0.698983 obj = -12.508575, rho = -0.114061 nSV = 72, nBSV = 67 Total nSV = 72 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 40 nu = 0.617387 obj = -14.241105, rho = -0.122503 nSV = 65, nBSV = 60 Total nSV = 65 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 38 nu = 0.557750 obj = -16.225029, rho = -0.138472 nSV = 56, nBSV = 53 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 47 nu = 0.511483 obj = -18.290531, rho = -0.069491 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 51 nu = 0.444810 obj = -20.580095, rho = -0.084306 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 30 nu = 0.394142 obj = -23.195068, rho = -0.132746 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 34 nu = 0.351435 obj = -26.090414, rho = -0.140196 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 63 nu = 0.309849 obj = -29.183818, rho = -0.123102 nSV = 37, nBSV = 28 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 85 nu = 0.271491 obj = -32.775330, rho = -0.177237 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.235941 obj = -36.915410, rho = -0.189070 nSV = 30, nBSV = 20 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 94 nu = 0.208752 obj = -41.809596, rho = -0.168307 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 40 nu = 0.183765 obj = -47.360057, rho = -0.123554 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 83 nu = 0.165553 obj = -53.556508, rho = -0.106283 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 68 nu = 0.146899 obj = -60.439545, rho = -0.076000 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 59 nu = 0.129006 obj = -68.524365, rho = -0.097983 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 70 nu = 0.116122 obj = -77.535706, rho = -0.145254 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -7.078939, rho = -0.247318 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 96% (96/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 47 nu = 0.899227 obj = -8.297312, rho = -0.108198 nSV = 91, nBSV = 88 Total nSV = 91 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -9.620037, rho = -0.059750 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 55 nu = 0.783030 obj = -11.005716, rho = -0.005322 nSV = 80, nBSV = 77 Total nSV = 80 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 86 nu = 0.699937 obj = -12.457976, rho = 0.018900 nSV = 74, nBSV = 67 Total nSV = 74 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.632391 obj = -14.101408, rho = 0.056809 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 43 nu = 0.564739 obj = -15.862568, rho = 0.052265 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 55 nu = 0.498305 obj = -17.778798, rho = 0.102569 nSV = 54, nBSV = 47 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 55 nu = 0.438235 obj = -19.951855, rho = 0.103290 nSV = 47, nBSV = 39 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 34 nu = 0.385677 obj = -22.440984, rho = 0.090598 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 48 nu = 0.344762 obj = -25.065625, rho = 0.035094 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 40 nu = 0.304165 obj = -27.832141, rho = 0.049065 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.266302 obj = -30.795457, rho = 0.034529 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 79 nu = 0.231361 obj = -34.149095, rho = 0.088289 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 74 nu = 0.201879 obj = -37.741935, rho = 0.141614 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 77 nu = 0.170873 obj = -41.851893, rho = 0.162887 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 85 nu = 0.149744 obj = -46.717227, rho = 0.199403 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 40 nu = 0.133019 obj = -52.099011, rho = 0.196631 nSV = 15, nBSV = 11 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 75 nu = 0.120879 obj = -56.815934, rho = 0.135469 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.105609 obj = -61.467573, rho = 0.133479 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -6.835300, rho = -0.375239 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 91% (91/100) (classification) Accuracy = 91.7% (917/1000) (classification) * optimization finished, #iter = 48 nu = 0.869366 obj = -7.986819, rho = -0.272352 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 41 nu = 0.801402 obj = -9.235465, rho = -0.229968 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 39 nu = 0.746896 obj = -10.592092, rho = -0.200989 nSV = 76, nBSV = 74 Total nSV = 76 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 57 nu = 0.684365 obj = -12.004418, rho = -0.192403 nSV = 70, nBSV = 67 Total nSV = 70 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 68 nu = 0.606876 obj = -13.503516, rho = -0.150855 nSV = 64, nBSV = 57 Total nSV = 64 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 41 nu = 0.540000 obj = -15.221895, rho = -0.107710 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 42 nu = 0.475143 obj = -17.070035, rho = -0.098466 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 63 nu = 0.427887 obj = -19.065338, rho = -0.045010 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 60 nu = 0.372466 obj = -21.240298, rho = -0.013577 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 40 nu = 0.334501 obj = -23.533182, rho = -0.136212 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 84 nu = 0.291325 obj = -25.823012, rho = -0.170285 nSV = 34, nBSV = 24 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 63 nu = 0.257612 obj = -28.193617, rho = -0.170043 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 189 nu = 0.216229 obj = -30.588448, rho = -0.162497 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 150 nu = 0.181987 obj = -33.375942, rho = -0.184407 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 123 nu = 0.155651 obj = -36.675765, rho = -0.170080 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.137007 obj = -40.135392, rho = -0.236656 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.120624 obj = -43.409793, rho = -0.296514 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 189 nu = 0.102337 obj = -46.248922, rho = -0.321178 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 131 nu = 0.087079 obj = -49.178129, rho = -0.369267 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 47 nu = 0.920000 obj = -7.065280, rho = -0.285520 nSV = 93, nBSV = 91 Total nSV = 93 Accuracy = 94% (94/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 43 nu = 0.860000 obj = -8.352576, rho = -0.219785 nSV = 86, nBSV = 86 Total nSV = 86 Accuracy = 95% (95/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 42 nu = 0.820000 obj = -9.793954, rho = -0.194662 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 96% (96/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 49 nu = 0.765181 obj = -11.362239, rho = -0.154410 nSV = 79, nBSV = 74 Total nSV = 79 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 40 nu = 0.711966 obj = -13.100401, rho = -0.145159 nSV = 73, nBSV = 70 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 42 nu = 0.650357 obj = -14.937374, rho = -0.108482 nSV = 68, nBSV = 63 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 50 nu = 0.583459 obj = -17.000557, rho = -0.068143 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.522562 obj = -19.271570, rho = -0.029173 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 58 nu = 0.462847 obj = -21.928456, rho = -0.008643 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.416615 obj = -24.939655, rho = 0.022423 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 53 nu = 0.364382 obj = -28.386103, rho = 0.037795 nSV = 41, nBSV = 33 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 48 nu = 0.328590 obj = -32.498935, rho = -0.037571 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 33 nu = 0.295581 obj = -37.118692, rho = -0.054301 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 63 nu = 0.260989 obj = -42.484745, rho = -0.034905 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 41 nu = 0.239313 obj = -48.496048, rho = 0.001036 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.214579 obj = -55.151406, rho = 0.008994 nSV = 23, nBSV = 19 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 71 nu = 0.191791 obj = -62.396404, rho = -0.030868 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 74 nu = 0.165914 obj = -71.154783, rho = -0.022064 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 61 nu = 0.151356 obj = -81.279785, rho = -0.005149 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 60 nu = 0.140220 obj = -91.937167, rho = 0.237185 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 41 nu = 0.820000 obj = -6.477490, rho = -0.575892 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 90% (90/100) (classification) Accuracy = 73.7% (737/1000) (classification) * optimization finished, #iter = 41 nu = 0.820000 obj = -7.652084, rho = -0.461709 nSV = 82, nBSV = 82 Total nSV = 82 Accuracy = 95% (95/100) (classification) Accuracy = 88.2% (882/1000) (classification) * optimization finished, #iter = 44 nu = 0.777697 obj = -8.872168, rho = -0.373763 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 96% (96/100) (classification) Accuracy = 92.9% (929/1000) (classification) * optimization finished, #iter = 39 nu = 0.703500 obj = -10.182928, rho = -0.315697 nSV = 73, nBSV = 69 Total nSV = 73 Accuracy = 97% (97/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 38 nu = 0.646146 obj = -11.631155, rho = -0.360956 nSV = 66, nBSV = 63 Total nSV = 66 Accuracy = 97% (97/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 40 nu = 0.575701 obj = -13.231908, rho = -0.357245 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 33 nu = 0.524904 obj = -15.057254, rho = -0.297567 nSV = 54, nBSV = 51 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 59 nu = 0.474936 obj = -16.943963, rho = -0.357927 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 62 nu = 0.412275 obj = -19.043038, rho = -0.370418 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 56 nu = 0.365235 obj = -21.511089, rho = -0.331178 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 67 nu = 0.322324 obj = -24.218061, rho = -0.305127 nSV = 36, nBSV = 27 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 80 nu = 0.280573 obj = -27.456458, rho = -0.329088 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 72 nu = 0.250717 obj = -31.210837, rho = -0.294331 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 29 nu = 0.222333 obj = -35.618060, rho = -0.263460 nSV = 26, nBSV = 21 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 54 nu = 0.202967 obj = -40.313363, rho = -0.202012 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 36 nu = 0.186152 obj = -45.336473, rho = -0.118458 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 66 nu = 0.164395 obj = -50.297525, rho = -0.154582 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 66 nu = 0.143868 obj = -55.792007, rho = -0.183863 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 52 nu = 0.127672 obj = -61.451855, rho = -0.140632 nSV = 14, nBSV = 9 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 77 nu = 0.109050 obj = -67.169673, rho = -0.211270 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 54 nu = 0.895503 obj = -6.556042, rho = -0.380344 nSV = 91, nBSV = 87 Total nSV = 91 Accuracy = 98% (98/100) (classification) Accuracy = 93.5% (935/1000) (classification) * optimization finished, #iter = 47 nu = 0.830774 obj = -7.635695, rho = -0.340950 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 99% (99/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 40 nu = 0.772475 obj = -8.828002, rho = -0.374049 nSV = 78, nBSV = 76 Total nSV = 78 Accuracy = 99% (99/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 38 nu = 0.727648 obj = -10.096095, rho = -0.392023 nSV = 74, nBSV = 72 Total nSV = 74 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 44 nu = 0.650107 obj = -11.405933, rho = -0.389056 nSV = 66, nBSV = 63 Total nSV = 66 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 44 nu = 0.576776 obj = -12.847430, rho = -0.377386 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 35 nu = 0.516611 obj = -14.460710, rho = -0.405731 nSV = 53, nBSV = 50 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 52 nu = 0.460452 obj = -16.115740, rho = -0.361775 nSV = 49, nBSV = 42 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 65 nu = 0.401505 obj = -17.933306, rho = -0.381936 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 69 nu = 0.354070 obj = -19.904831, rho = -0.357915 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 57 nu = 0.309542 obj = -21.994907, rho = -0.322023 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 56 nu = 0.267727 obj = -24.287398, rho = -0.301544 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 41 nu = 0.237721 obj = -26.765201, rho = -0.301592 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 69 nu = 0.208916 obj = -29.034324, rho = -0.245209 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 144 nu = 0.178144 obj = -31.342060, rho = -0.213377 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..*..* optimization finished, #iter = 484 nu = 0.148959 obj = -33.799662, rho = -0.214713 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 129 nu = 0.129410 obj = -36.515255, rho = -0.271221 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.114859 obj = -38.638808, rho = -0.326748 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 207 nu = 0.094387 obj = -40.146099, rho = -0.345381 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..*.* optimization finished, #iter = 383 nu = 0.076559 obj = -41.665027, rho = -0.363735 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -6.903774, rho = -0.125435 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 89% (89/100) (classification) Accuracy = 86.6% (866/1000) (classification) * optimization finished, #iter = 48 nu = 0.851645 obj = -8.152824, rho = -0.271361 nSV = 86, nBSV = 83 Total nSV = 86 Accuracy = 93% (93/100) (classification) Accuracy = 90.8% (908/1000) (classification) * optimization finished, #iter = 44 nu = 0.792436 obj = -9.564622, rho = -0.321190 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 94% (94/100) (classification) Accuracy = 93.4% (934/1000) (classification) * optimization finished, #iter = 44 nu = 0.733757 obj = -11.178028, rho = -0.312382 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 94% (94/100) (classification) Accuracy = 93.5% (935/1000) (classification) * optimization finished, #iter = 40 nu = 0.691162 obj = -12.996845, rho = -0.308140 nSV = 70, nBSV = 67 Total nSV = 70 Accuracy = 94% (94/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 41 nu = 0.638607 obj = -14.970826, rho = -0.279661 nSV = 65, nBSV = 62 Total nSV = 65 Accuracy = 96% (96/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 48 nu = 0.584006 obj = -17.126030, rho = -0.241480 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 46 nu = 0.526509 obj = -19.512988, rho = -0.201710 nSV = 55, nBSV = 52 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 85 nu = 0.480000 obj = -22.004988, rho = -0.214901 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 34 nu = 0.430660 obj = -24.602444, rho = -0.277725 nSV = 44, nBSV = 41 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.370260 obj = -27.470767, rho = -0.286156 nSV = 43, nBSV = 33 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.326422 obj = -30.956520, rho = -0.287855 nSV = 35, nBSV = 31 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 75 nu = 0.287517 obj = -34.732875, rho = -0.302611 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 83 nu = 0.255714 obj = -38.900553, rho = -0.259863 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 48 nu = 0.223339 obj = -43.664563, rho = -0.288624 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 73 nu = 0.198421 obj = -48.914689, rho = -0.314281 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 70 nu = 0.174929 obj = -54.617322, rho = -0.433770 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 91 nu = 0.156186 obj = -60.542724, rho = -0.502960 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 169 nu = 0.136229 obj = -66.958464, rho = -0.520275 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 132 nu = 0.118959 obj = -73.909878, rho = -0.532186 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -7.062875, rho = -0.121986 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 96% (96/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 45 nu = 0.896764 obj = -8.256425, rho = -0.207054 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 49 nu = 0.843605 obj = -9.537360, rho = -0.229899 nSV = 86, nBSV = 82 Total nSV = 86 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.769071 obj = -10.915462, rho = -0.221130 nSV = 78, nBSV = 74 Total nSV = 78 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.700000 obj = -12.418455, rho = -0.211464 nSV = 73, nBSV = 69 Total nSV = 73 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 50 nu = 0.628588 obj = -14.016503, rho = -0.177397 nSV = 65, nBSV = 59 Total nSV = 65 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 37 nu = 0.557979 obj = -15.824365, rho = -0.173799 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 51 nu = 0.491592 obj = -17.823713, rho = -0.191757 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 55 nu = 0.437827 obj = -20.076530, rho = -0.197712 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 41 nu = 0.385696 obj = -22.634810, rho = -0.232823 nSV = 40, nBSV = 36 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 33 nu = 0.340354 obj = -25.487956, rho = -0.245899 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 53 nu = 0.305905 obj = -28.672579, rho = -0.206562 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 31 nu = 0.267200 obj = -32.162850, rho = -0.209824 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 33 nu = 0.243323 obj = -35.822868, rho = -0.274016 nSV = 27, nBSV = 23 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 52 nu = 0.214970 obj = -39.343285, rho = -0.267534 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 44 nu = 0.184941 obj = -43.066824, rho = -0.281818 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 57 nu = 0.164478 obj = -46.588417, rho = -0.320129 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.136579 obj = -50.179799, rho = -0.275073 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 74 nu = 0.114724 obj = -54.317360, rho = -0.278087 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 277 nu = 0.096074 obj = -59.273996, rho = -0.287297 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 50 nu = 0.976797 obj = -7.213295, rho = -0.136447 nSV = 98, nBSV = 95 Total nSV = 98 Accuracy = 97% (97/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 46 nu = 0.901661 obj = -8.428175, rho = -0.071070 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 47 nu = 0.840000 obj = -9.793009, rho = -0.046645 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 96% (96/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 43 nu = 0.776631 obj = -11.302779, rho = 0.011044 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 43 nu = 0.702971 obj = -12.984540, rho = -0.039099 nSV = 73, nBSV = 68 Total nSV = 73 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 41 nu = 0.640452 obj = -14.851842, rho = -0.092291 nSV = 68, nBSV = 61 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 37 nu = 0.579039 obj = -16.953949, rho = -0.102522 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 61 nu = 0.525652 obj = -19.274683, rho = -0.078545 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 0.468258 obj = -21.805970, rho = -0.012800 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 75 nu = 0.412487 obj = -24.680062, rho = 0.005476 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 78 nu = 0.366143 obj = -27.977294, rho = 0.028743 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 82 nu = 0.322782 obj = -31.804599, rho = 0.056058 nSV = 35, nBSV = 30 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.295892 obj = -36.182311, rho = 0.037435 nSV = 31, nBSV = 28 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 71 nu = 0.270835 obj = -40.647120, rho = 0.074063 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 53 nu = 0.241967 obj = -45.104883, rho = 0.053207 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 58 nu = 0.211957 obj = -49.723683, rho = 0.117220 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 281 nu = 0.182500 obj = -54.270955, rho = 0.144410 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 133 nu = 0.154683 obj = -59.758991, rho = 0.160361 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 151 nu = 0.133162 obj = -65.810461, rho = 0.165013 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.115374 obj = -72.977302, rho = 0.160152 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 47 nu = 0.931394 obj = -6.941775, rho = -0.151713 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 97% (97/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 46 nu = 0.869607 obj = -8.125704, rho = -0.220797 nSV = 89, nBSV = 86 Total nSV = 89 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 48 nu = 0.816772 obj = -9.434984, rho = -0.174720 nSV = 83, nBSV = 80 Total nSV = 83 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 44 nu = 0.754277 obj = -10.873268, rho = -0.179257 nSV = 76, nBSV = 73 Total nSV = 76 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 48 nu = 0.682798 obj = -12.448597, rho = -0.144954 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 39 nu = 0.626200 obj = -14.148182, rho = -0.233764 nSV = 64, nBSV = 61 Total nSV = 64 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 51 nu = 0.550926 obj = -16.021394, rho = -0.195911 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.490794 obj = -18.244638, rho = -0.182068 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 67 nu = 0.440000 obj = -20.769740, rho = -0.227413 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 43 nu = 0.392713 obj = -23.646323, rho = -0.225033 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 26 nu = 0.361672 obj = -26.672330, rho = -0.271433 nSV = 38, nBSV = 34 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.320924 obj = -29.673081, rho = -0.252469 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 78 nu = 0.282287 obj = -33.063383, rho = -0.254792 nSV = 32, nBSV = 23 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.242277 obj = -36.833842, rho = -0.250545 nSV = 29, nBSV = 19 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 62 nu = 0.213361 obj = -41.298767, rho = -0.251849 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 46 nu = 0.191145 obj = -46.033263, rho = -0.336170 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 68 nu = 0.167929 obj = -50.985606, rho = -0.448783 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 74 nu = 0.144500 obj = -56.295351, rho = -0.535513 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 53 nu = 0.124352 obj = -62.461264, rho = -0.619729 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.107921 obj = -69.380728, rho = -0.676353 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -6.549320, rho = 0.153618 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 85% (85/100) (classification) Accuracy = 82.6% (826/1000) (classification) * optimization finished, #iter = 47 nu = 0.819105 obj = -7.708683, rho = -0.007348 nSV = 83, nBSV = 79 Total nSV = 83 Accuracy = 91% (91/100) (classification) Accuracy = 92.6% (926/1000) (classification) * optimization finished, #iter = 40 nu = 0.756187 obj = -9.001374, rho = -0.034114 nSV = 76, nBSV = 74 Total nSV = 76 Accuracy = 94% (94/100) (classification) Accuracy = 94.5% (945/1000) (classification) * optimization finished, #iter = 42 nu = 0.705223 obj = -10.464848, rho = -0.051799 nSV = 72, nBSV = 69 Total nSV = 72 Accuracy = 96% (96/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 44 nu = 0.654805 obj = -12.059790, rho = -0.148369 nSV = 68, nBSV = 63 Total nSV = 68 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 42 nu = 0.587625 obj = -13.852157, rho = -0.171746 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 39 nu = 0.533698 obj = -15.893229, rho = -0.178070 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 62 nu = 0.482612 obj = -18.174432, rho = -0.248952 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 96% (96/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 58 nu = 0.430419 obj = -20.785764, rho = -0.258929 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 41 nu = 0.383148 obj = -23.881132, rho = -0.243844 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 67 nu = 0.355944 obj = -27.327996, rho = -0.377392 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 58 nu = 0.313307 obj = -31.154834, rho = -0.366101 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 83 nu = 0.285480 obj = -35.552765, rho = -0.361359 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 52 nu = 0.254878 obj = -40.522948, rho = -0.321342 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 146 nu = 0.230592 obj = -45.721453, rho = -0.291676 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 89 nu = 0.201839 obj = -51.805250, rho = -0.270339 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.185236 obj = -58.396754, rho = -0.213573 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 141 nu = 0.161930 obj = -65.318232, rho = -0.244622 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.144365 obj = -73.194709, rho = -0.223668 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.127126 obj = -81.456460, rho = -0.173789 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.900000 obj = -7.149195, rho = -0.407514 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 85% (85/100) (classification) Accuracy = 89.2% (892/1000) (classification) * optimization finished, #iter = 49 nu = 0.877801 obj = -8.480921, rho = -0.334198 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 91% (91/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 47 nu = 0.826458 obj = -9.969451, rho = -0.272077 nSV = 84, nBSV = 80 Total nSV = 84 Accuracy = 96% (96/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 41 nu = 0.777123 obj = -11.632633, rho = -0.189985 nSV = 78, nBSV = 76 Total nSV = 78 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 54 nu = 0.718075 obj = -13.437530, rho = -0.133802 nSV = 75, nBSV = 70 Total nSV = 75 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 66 nu = 0.661515 obj = -15.434598, rho = -0.129024 nSV = 69, nBSV = 63 Total nSV = 69 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 38 nu = 0.598113 obj = -17.698838, rho = -0.114861 nSV = 61, nBSV = 58 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 47 nu = 0.535588 obj = -20.198302, rho = -0.113527 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 60 nu = 0.477515 obj = -23.133177, rho = -0.071455 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 51 nu = 0.423581 obj = -26.590097, rho = -0.081722 nSV = 47, nBSV = 40 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 41 nu = 0.381867 obj = -30.660763, rho = -0.080332 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 41 nu = 0.346969 obj = -35.348470, rho = -0.060038 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 32 nu = 0.318912 obj = -40.783793, rho = -0.142529 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 29 nu = 0.296750 obj = -46.334852, rho = -0.088520 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 88 nu = 0.267612 obj = -52.167398, rho = -0.062375 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 73 nu = 0.237073 obj = -58.373776, rho = -0.154980 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 72 nu = 0.206230 obj = -65.549058, rho = -0.159502 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 75 nu = 0.183998 obj = -73.410942, rho = -0.238796 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *..* optimization finished, #iter = 209 nu = 0.167404 obj = -81.070145, rho = -0.346885 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *..............* optimization finished, #iter = 1445 nu = 0.144440 obj = -88.601580, rho = -0.351767 nSV = 18, nBSV = 7 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 44 nu = 0.880000 obj = -6.804484, rho = 0.087349 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 93% (93/100) (classification) Accuracy = 90.4% (904/1000) (classification) * optimization finished, #iter = 43 nu = 0.855563 obj = -8.014469, rho = -0.019522 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 98% (98/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 41 nu = 0.810074 obj = -9.292914, rho = 0.015397 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 42 nu = 0.736039 obj = -10.700061, rho = -0.010704 nSV = 74, nBSV = 72 Total nSV = 74 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 39 nu = 0.665526 obj = -12.313612, rho = -0.046849 nSV = 69, nBSV = 66 Total nSV = 69 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 51 nu = 0.622599 obj = -13.999306, rho = -0.054172 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 51 nu = 0.551752 obj = -15.781489, rho = -0.027854 nSV = 59, nBSV = 52 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 80 nu = 0.494974 obj = -17.787945, rho = -0.048435 nSV = 53, nBSV = 46 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 51 nu = 0.434765 obj = -19.986821, rho = -0.066778 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 63 nu = 0.382838 obj = -22.475056, rho = -0.047383 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 56 nu = 0.344302 obj = -25.266813, rho = 0.031821 nSV = 38, nBSV = 30 Total nSV = 38 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 72 nu = 0.298422 obj = -28.343825, rho = 0.049103 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.258891 obj = -31.929052, rho = 0.041229 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 69 nu = 0.231587 obj = -36.137532, rho = 0.084643 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 64 nu = 0.202155 obj = -41.039407, rho = 0.086629 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 65 nu = 0.184267 obj = -46.543442, rho = 0.022443 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.162632 obj = -52.562858, rho = -0.042034 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 167 nu = 0.140237 obj = -59.787872, rho = -0.071559 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 274 nu = 0.125247 obj = -68.304354, rho = -0.151613 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 181 nu = 0.109006 obj = -78.783438, rho = -0.154845 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 45 nu = 0.840000 obj = -6.782723, rho = 0.306557 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 79% (79/100) (classification) Accuracy = 78.3% (783/1000) (classification) * optimization finished, #iter = 44 nu = 0.826177 obj = -8.082262, rho = 0.169802 nSV = 84, nBSV = 82 Total nSV = 84 Accuracy = 92% (92/100) (classification) Accuracy = 91.2% (912/1000) (classification) * optimization finished, #iter = 42 nu = 0.807787 obj = -9.476401, rho = 0.088214 nSV = 82, nBSV = 80 Total nSV = 82 Accuracy = 98% (98/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 52 nu = 0.758019 obj = -10.936658, rho = 0.061469 nSV = 77, nBSV = 73 Total nSV = 77 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 45 nu = 0.700000 obj = -12.499948, rho = 0.009334 nSV = 71, nBSV = 68 Total nSV = 71 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 60 nu = 0.628282 obj = -14.176415, rho = 0.046365 nSV = 66, nBSV = 60 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 40 nu = 0.564861 obj = -16.029265, rho = 0.076302 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 42 nu = 0.503088 obj = -18.007731, rho = 0.080106 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 44 nu = 0.450245 obj = -20.152956, rho = -0.010435 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 43 nu = 0.397719 obj = -22.395107, rho = -0.016496 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 79 nu = 0.343857 obj = -24.865523, rho = -0.088514 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 54 nu = 0.298869 obj = -27.684400, rho = -0.101819 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 45 nu = 0.259377 obj = -30.882062, rho = -0.150147 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 84 nu = 0.221034 obj = -34.753771, rho = -0.156543 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.197405 obj = -39.401230, rho = -0.114252 nSV = 22, nBSV = 18 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 66 nu = 0.173990 obj = -44.515838, rho = -0.073393 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 64 nu = 0.157150 obj = -50.313970, rho = -0.146022 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 89 nu = 0.138190 obj = -56.625409, rho = -0.185370 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 182 nu = 0.122458 obj = -63.737062, rho = -0.195090 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 80 nu = 0.111154 obj = -71.724785, rho = -0.311154 nSV = 13, nBSV = 8 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 51 nu = 0.865553 obj = -6.207968, rho = -0.221161 nSV = 89, nBSV = 85 Total nSV = 89 Accuracy = 97% (97/100) (classification) Accuracy = 94.8% (948/1000) (classification) * optimization finished, #iter = 40 nu = 0.800000 obj = -7.186536, rho = -0.231327 nSV = 80, nBSV = 80 Total nSV = 80 Accuracy = 97% (97/100) (classification) Accuracy = 95.1% (951/1000) (classification) * optimization finished, #iter = 41 nu = 0.729055 obj = -8.234077, rho = -0.211820 nSV = 75, nBSV = 72 Total nSV = 75 Accuracy = 97% (97/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 39 nu = 0.667240 obj = -9.392219, rho = -0.203675 nSV = 68, nBSV = 65 Total nSV = 68 Accuracy = 97% (97/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 40 nu = 0.608206 obj = -10.620707, rho = -0.218205 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 42 nu = 0.539051 obj = -11.984879, rho = -0.270350 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 67 nu = 0.469701 obj = -13.534662, rho = -0.250670 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 61 nu = 0.421196 obj = -15.321001, rho = -0.190899 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 55 nu = 0.375752 obj = -17.320431, rho = -0.164077 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 53 nu = 0.336332 obj = -19.513330, rho = -0.246101 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 73 nu = 0.293662 obj = -21.896323, rho = -0.232022 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 40 nu = 0.260000 obj = -24.673960, rho = -0.210730 nSV = 29, nBSV = 25 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 42 nu = 0.230984 obj = -27.619782, rho = -0.174757 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 53 nu = 0.203366 obj = -30.948413, rho = -0.155186 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 80 nu = 0.179108 obj = -34.676981, rho = -0.083923 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 88 nu = 0.155545 obj = -38.903901, rho = -0.103416 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 54 nu = 0.140052 obj = -43.647498, rho = -0.087966 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 73 nu = 0.123787 obj = -48.574227, rho = 0.017448 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 95 nu = 0.108178 obj = -54.068971, rho = 0.128717 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.094516 obj = -59.804597, rho = 0.183130 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 48 nu = 0.940000 obj = -6.983062, rho = -0.224876 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 98% (98/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 47 nu = 0.900000 obj = -8.101395, rho = -0.102300 nSV = 91, nBSV = 89 Total nSV = 91 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 48 nu = 0.837344 obj = -9.280127, rho = -0.033530 nSV = 85, nBSV = 81 Total nSV = 85 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 55 nu = 0.749449 obj = -10.555119, rho = -0.028133 nSV = 78, nBSV = 73 Total nSV = 78 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.676551 obj = -11.975617, rho = -0.023836 nSV = 69, nBSV = 64 Total nSV = 69 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 41 nu = 0.600432 obj = -13.572438, rho = 0.001482 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 39 nu = 0.539835 obj = -15.305503, rho = -0.085290 nSV = 58, nBSV = 51 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 35 nu = 0.476455 obj = -17.231977, rho = -0.090458 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 51 nu = 0.432776 obj = -19.319340, rho = -0.156592 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 51 nu = 0.374285 obj = -21.564853, rho = -0.152658 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 69 nu = 0.329586 obj = -24.048102, rho = -0.118837 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 63 nu = 0.289634 obj = -26.785158, rho = -0.122211 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 49 nu = 0.251164 obj = -29.939594, rho = -0.153504 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 69 nu = 0.218412 obj = -33.596384, rho = -0.177335 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 58 nu = 0.189058 obj = -37.870389, rho = -0.203223 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.170981 obj = -42.792153, rho = -0.271664 nSV = 20, nBSV = 15 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 39 nu = 0.156010 obj = -47.804585, rho = -0.291117 nSV = 18, nBSV = 13 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 66 nu = 0.141507 obj = -52.405143, rho = -0.294564 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.121820 obj = -56.580296, rho = -0.283845 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 282 nu = 0.103563 obj = -60.852698, rho = -0.281740 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 46 nu = 0.900000 obj = -7.086155, rho = -0.346086 nSV = 90, nBSV = 90 Total nSV = 90 Accuracy = 90% (90/100) (classification) Accuracy = 89.8% (898/1000) (classification) * optimization finished, #iter = 45 nu = 0.880000 obj = -8.370171, rho = -0.231251 nSV = 88, nBSV = 88 Total nSV = 88 Accuracy = 96% (96/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 51 nu = 0.831549 obj = -9.746677, rho = -0.166218 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 46 nu = 0.775273 obj = -11.229971, rho = -0.105153 nSV = 80, nBSV = 76 Total nSV = 80 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 37 nu = 0.703873 obj = -12.846493, rho = -0.092431 nSV = 72, nBSV = 70 Total nSV = 72 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 57 nu = 0.629325 obj = -14.684118, rho = -0.154935 nSV = 65, nBSV = 59 Total nSV = 65 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.570041 obj = -16.842024, rho = -0.170867 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 36 nu = 0.515528 obj = -19.227388, rho = -0.125340 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 49 nu = 0.460966 obj = -21.852496, rho = -0.149281 nSV = 50, nBSV = 43 Total nSV = 50 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 69 nu = 0.411621 obj = -24.855652, rho = -0.180191 nSV = 46, nBSV = 38 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 44 nu = 0.369829 obj = -28.314394, rho = -0.170441 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.331960 obj = -32.056339, rho = -0.113784 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 81 nu = 0.295983 obj = -36.380511, rho = -0.081496 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 87 nu = 0.259332 obj = -41.317365, rho = -0.115016 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 167 nu = 0.229977 obj = -47.023648, rho = -0.081613 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 201 nu = 0.208634 obj = -53.408821, rho = 0.013365 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.184770 obj = -60.695590, rho = 0.071960 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 179 nu = 0.161557 obj = -69.266758, rho = 0.101758 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 195 nu = 0.142963 obj = -79.559790, rho = 0.125928 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.126747 obj = -92.093619, rho = 0.108741 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.852727 obj = -6.281948, rho = -0.329961 nSV = 88, nBSV = 84 Total nSV = 88 Accuracy = 97% (97/100) (classification) Accuracy = 92.6% (926/1000) (classification) * optimization finished, #iter = 43 nu = 0.781381 obj = -7.335054, rho = -0.331828 nSV = 80, nBSV = 76 Total nSV = 80 Accuracy = 98% (98/100) (classification) Accuracy = 93.6% (936/1000) (classification) * optimization finished, #iter = 39 nu = 0.728613 obj = -8.550170, rho = -0.265452 nSV = 74, nBSV = 72 Total nSV = 74 Accuracy = 98% (98/100) (classification) Accuracy = 94.4% (944/1000) (classification) * optimization finished, #iter = 43 nu = 0.666380 obj = -9.924683, rho = -0.252628 nSV = 68, nBSV = 63 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 94.8% (948/1000) (classification) * optimization finished, #iter = 43 nu = 0.614117 obj = -11.489441, rho = -0.195291 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 98% (98/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 31 nu = 0.564061 obj = -13.201254, rho = -0.151031 nSV = 58, nBSV = 56 Total nSV = 58 Accuracy = 96% (96/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 35 nu = 0.513965 obj = -15.021744, rho = -0.123631 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 96% (96/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 42 nu = 0.460878 obj = -17.061030, rho = -0.140729 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 97% (97/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 32 nu = 0.424238 obj = -19.286252, rho = -0.110867 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 96% (96/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 46 nu = 0.377758 obj = -21.591394, rho = -0.149195 nSV = 41, nBSV = 33 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 79 nu = 0.333119 obj = -24.031787, rho = -0.142193 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 38 nu = 0.289217 obj = -26.777027, rho = -0.161831 nSV = 31, nBSV = 27 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.251245 obj = -29.857129, rho = -0.152278 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 86 nu = 0.215335 obj = -33.471453, rho = -0.144691 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 97% (97/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 96 nu = 0.189107 obj = -37.793732, rho = -0.186773 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 78 nu = 0.165727 obj = -42.953835, rho = -0.236532 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 80 nu = 0.151453 obj = -48.808311, rho = -0.380157 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 54 nu = 0.138486 obj = -54.887526, rho = -0.463870 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 95.7% (957/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.125254 obj = -60.423061, rho = -0.453348 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 95% (950/1000) (classification) .*.* optimization finished, #iter = 229 nu = 0.107975 obj = -66.197299, rho = -0.502695 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 95.2% (952/1000) (classification) * optimization finished, #iter = 46 nu = 0.906112 obj = -6.832295, rho = -0.169236 nSV = 92, nBSV = 90 Total nSV = 92 Accuracy = 99% (99/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 48 nu = 0.847331 obj = -8.007942, rho = -0.216464 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 98% (98/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 40 nu = 0.799685 obj = -9.337553, rho = -0.317445 nSV = 80, nBSV = 78 Total nSV = 80 Accuracy = 98% (98/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 40 nu = 0.727729 obj = -10.816826, rho = -0.291106 nSV = 75, nBSV = 71 Total nSV = 75 Accuracy = 98% (98/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 37 nu = 0.672594 obj = -12.515320, rho = -0.294624 nSV = 68, nBSV = 65 Total nSV = 68 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 38 nu = 0.613629 obj = -14.420006, rho = -0.285421 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 41 nu = 0.554645 obj = -16.569417, rho = -0.295746 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 41 nu = 0.509193 obj = -18.931933, rho = -0.277230 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 48 nu = 0.460000 obj = -21.501979, rho = -0.339428 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 37 nu = 0.406406 obj = -24.340435, rho = -0.372879 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 42 nu = 0.365766 obj = -27.576237, rho = -0.376894 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 37 nu = 0.329611 obj = -30.998075, rho = -0.431826 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 54 nu = 0.288760 obj = -34.836355, rho = -0.416406 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 64 nu = 0.252155 obj = -39.226761, rho = -0.445311 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 59 nu = 0.232378 obj = -44.029935, rho = -0.394131 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 162 nu = 0.202336 obj = -48.738974, rho = -0.395627 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.175397 obj = -54.181338, rho = -0.364816 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 118 nu = 0.152710 obj = -60.497338, rho = -0.410339 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 192 nu = 0.132818 obj = -67.514619, rho = -0.412059 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *..* optimization finished, #iter = 252 nu = 0.115213 obj = -75.550543, rho = -0.411291 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.840000 obj = -6.780943, rho = 0.343888 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 79% (79/100) (classification) Accuracy = 74.7% (747/1000) (classification) * optimization finished, #iter = 43 nu = 0.840000 obj = -8.074923, rho = 0.163932 nSV = 85, nBSV = 83 Total nSV = 85 Accuracy = 94% (94/100) (classification) Accuracy = 91.1% (911/1000) (classification) * optimization finished, #iter = 44 nu = 0.820000 obj = -9.438613, rho = 0.091273 nSV = 83, nBSV = 81 Total nSV = 83 Accuracy = 98% (98/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 44 nu = 0.760000 obj = -10.835626, rho = 0.050212 nSV = 78, nBSV = 74 Total nSV = 78 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 53 nu = 0.686856 obj = -12.327088, rho = -0.023000 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 41 nu = 0.620721 obj = -13.976132, rho = 0.028652 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.550334 obj = -15.776254, rho = 0.028370 nSV = 57, nBSV = 50 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 52 nu = 0.490999 obj = -17.879441, rho = -0.009675 nSV = 53, nBSV = 46 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 88 nu = 0.433558 obj = -20.200217, rho = -0.014817 nSV = 47, nBSV = 40 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 53 nu = 0.380128 obj = -22.941621, rho = -0.006530 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 63 nu = 0.342022 obj = -26.018753, rho = 0.016590 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 68 nu = 0.310944 obj = -29.319740, rho = 0.055229 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 88 nu = 0.273343 obj = -32.856720, rho = 0.044725 nSV = 32, nBSV = 23 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 49 nu = 0.243309 obj = -36.803934, rho = 0.028254 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 81 nu = 0.211236 obj = -41.156309, rho = 0.001468 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 84 nu = 0.186126 obj = -46.207830, rho = -0.021164 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 65 nu = 0.163033 obj = -51.770860, rho = 0.044919 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 93 nu = 0.143951 obj = -58.113091, rho = 0.164966 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.124954 obj = -65.417553, rho = 0.197109 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.110008 obj = -74.025791, rho = 0.216886 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 53 nu = 0.877279 obj = -6.401824, rho = -0.431193 nSV = 89, nBSV = 85 Total nSV = 89 Accuracy = 94% (94/100) (classification) Accuracy = 92.9% (929/1000) (classification) * optimization finished, #iter = 47 nu = 0.811334 obj = -7.448146, rho = -0.399464 nSV = 83, nBSV = 78 Total nSV = 83 Accuracy = 96% (96/100) (classification) Accuracy = 94.2% (942/1000) (classification) * optimization finished, #iter = 43 nu = 0.752528 obj = -8.603125, rho = -0.357261 nSV = 77, nBSV = 74 Total nSV = 77 Accuracy = 98% (98/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 39 nu = 0.682923 obj = -9.871427, rho = -0.336183 nSV = 71, nBSV = 67 Total nSV = 71 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 41 nu = 0.622097 obj = -11.309798, rho = -0.373092 nSV = 64, nBSV = 61 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 39 nu = 0.567988 obj = -12.886506, rho = -0.327793 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 61 nu = 0.508079 obj = -14.593036, rho = -0.341654 nSV = 54, nBSV = 47 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 41 nu = 0.457109 obj = -16.492973, rho = -0.288224 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 38 nu = 0.398773 obj = -18.648345, rho = -0.285196 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 95 nu = 0.362143 obj = -21.017247, rho = -0.264929 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.315564 obj = -23.575059, rho = -0.248974 nSV = 36, nBSV = 27 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.274818 obj = -26.605653, rho = -0.282953 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 83 nu = 0.241350 obj = -30.278315, rho = -0.288251 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 75 nu = 0.217270 obj = -34.459784, rho = -0.246085 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.194165 obj = -39.144153, rho = -0.238805 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 140 nu = 0.175487 obj = -44.348394, rho = -0.262306 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 78 nu = 0.156243 obj = -49.966210, rho = -0.247160 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 74 nu = 0.140404 obj = -55.887747, rho = -0.237080 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 84 nu = 0.124181 obj = -62.411289, rho = -0.232430 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.108806 obj = -69.379297, rho = -0.173222 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 47 nu = 0.940000 obj = -7.199667, rho = -0.192515 nSV = 94, nBSV = 94 Total nSV = 94 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 45 nu = 0.889089 obj = -8.500163, rho = -0.183566 nSV = 90, nBSV = 88 Total nSV = 90 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 43 nu = 0.847363 obj = -9.926530, rho = -0.130580 nSV = 86, nBSV = 84 Total nSV = 86 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 44 nu = 0.789609 obj = -11.457193, rho = -0.134707 nSV = 81, nBSV = 78 Total nSV = 81 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 38 nu = 0.717706 obj = -13.106302, rho = -0.176592 nSV = 72, nBSV = 70 Total nSV = 72 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 52 nu = 0.640000 obj = -15.003465, rho = -0.166127 nSV = 67, nBSV = 62 Total nSV = 67 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 66 nu = 0.577246 obj = -17.178104, rho = -0.128615 nSV = 61, nBSV = 54 Total nSV = 61 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 40 nu = 0.522571 obj = -19.705163, rho = -0.096138 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.470524 obj = -22.490185, rho = -0.067676 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 58 nu = 0.430664 obj = -25.549511, rho = 0.020247 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 56 nu = 0.386567 obj = -28.810804, rho = -0.033204 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.340811 obj = -32.336837, rho = -0.027148 nSV = 38, nBSV = 29 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 66 nu = 0.296734 obj = -36.476301, rho = -0.052677 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 95 nu = 0.267395 obj = -41.130480, rho = -0.079663 nSV = 30, nBSV = 21 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.229872 obj = -46.508923, rho = -0.064821 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 52 nu = 0.205837 obj = -52.911702, rho = -0.031958 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 54 nu = 0.184553 obj = -59.933443, rho = 0.009113 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 61 nu = 0.161732 obj = -67.965352, rho = 0.015564 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 151 nu = 0.142117 obj = -77.630422, rho = -0.001434 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 54 nu = 0.124636 obj = -89.583455, rho = -0.000127 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 49 nu = 0.900000 obj = -7.148791, rho = 0.358767 nSV = 92, nBSV = 88 Total nSV = 92 Accuracy = 84% (84/100) (classification) Accuracy = 82.6% (826/1000) (classification) * optimization finished, #iter = 52 nu = 0.900000 obj = -8.462526, rho = 0.182425 nSV = 92, nBSV = 88 Total nSV = 92 Accuracy = 95% (95/100) (classification) Accuracy = 94.8% (948/1000) (classification) * optimization finished, #iter = 54 nu = 0.842858 obj = -9.822293, rho = 0.120058 nSV = 87, nBSV = 81 Total nSV = 87 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 45 nu = 0.779992 obj = -11.332108, rho = 0.054280 nSV = 79, nBSV = 75 Total nSV = 79 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 51 nu = 0.714681 obj = -13.011442, rho = 0.056032 nSV = 73, nBSV = 68 Total nSV = 73 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 45 nu = 0.647493 obj = -14.838623, rho = 0.014470 nSV = 67, nBSV = 61 Total nSV = 67 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 40 nu = 0.589941 obj = -16.872416, rho = 0.007629 nSV = 60, nBSV = 57 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 40 nu = 0.533971 obj = -18.994138, rho = 0.011632 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 50 nu = 0.462560 obj = -21.299716, rho = 0.021932 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 51 nu = 0.408300 obj = -24.000907, rho = 0.018543 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 62 nu = 0.365330 obj = -27.019194, rho = 0.037529 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 49 nu = 0.312724 obj = -30.510811, rho = 0.051116 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 41 nu = 0.280455 obj = -34.615948, rho = 0.086642 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 81 nu = 0.250718 obj = -39.179380, rho = 0.043368 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 95 nu = 0.220582 obj = -44.371329, rho = 0.013212 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 73 nu = 0.190744 obj = -50.682766, rho = 0.022020 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 56 nu = 0.172120 obj = -58.279919, rho = 0.000939 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 59 nu = 0.154365 obj = -67.034682, rho = -0.040111 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) *....* optimization finished, #iter = 434 nu = 0.138224 obj = -77.282617, rho = -0.095234 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.*.* optimization finished, #iter = 268 nu = 0.125148 obj = -89.494422, rho = -0.140007 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -6.935695, rho = 0.403929 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 76% (76/100) (classification) Accuracy = 75.1% (751/1000) (classification) * optimization finished, #iter = 45 nu = 0.860000 obj = -8.256305, rho = 0.240442 nSV = 87, nBSV = 85 Total nSV = 87 Accuracy = 90% (90/100) (classification) Accuracy = 91.1% (911/1000) (classification) * optimization finished, #iter = 43 nu = 0.838148 obj = -9.610297, rho = 0.101942 nSV = 84, nBSV = 82 Total nSV = 84 Accuracy = 95% (95/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 47 nu = 0.759776 obj = -11.084798, rho = 0.073286 nSV = 78, nBSV = 73 Total nSV = 78 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 51 nu = 0.684964 obj = -12.762603, rho = 0.018385 nSV = 71, nBSV = 66 Total nSV = 71 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 49 nu = 0.637557 obj = -14.649828, rho = -0.075761 nSV = 66, nBSV = 61 Total nSV = 66 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 39 nu = 0.572572 obj = -16.679666, rho = -0.116486 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 64 nu = 0.504999 obj = -18.979308, rho = -0.116865 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 48 nu = 0.451155 obj = -21.725715, rho = -0.120638 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 35 nu = 0.401861 obj = -24.960587, rho = -0.122456 nSV = 43, nBSV = 39 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 38 nu = 0.370586 obj = -28.573855, rho = -0.202681 nSV = 38, nBSV = 34 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 69 nu = 0.326761 obj = -32.557217, rho = -0.189734 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.296165 obj = -37.089797, rho = -0.235159 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 60 nu = 0.265585 obj = -42.142046, rho = -0.220313 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 56 nu = 0.234513 obj = -48.049845, rho = -0.235663 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 43 nu = 0.208677 obj = -55.044862, rho = -0.210418 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 42 nu = 0.193114 obj = -62.712644, rho = -0.145527 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 58 nu = 0.167102 obj = -71.348624, rho = -0.133765 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 62 nu = 0.154853 obj = -81.451565, rho = -0.032075 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.136837 obj = -92.027273, rho = 0.052964 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 47 nu = 0.924736 obj = -6.995699, rho = -0.190349 nSV = 94, nBSV = 92 Total nSV = 94 Accuracy = 95% (95/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 47 nu = 0.872315 obj = -8.211051, rho = -0.169046 nSV = 88, nBSV = 86 Total nSV = 88 Accuracy = 96% (96/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 44 nu = 0.820800 obj = -9.553670, rho = -0.076553 nSV = 84, nBSV = 81 Total nSV = 84 Accuracy = 96% (96/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 47 nu = 0.744753 obj = -11.050807, rho = -0.092503 nSV = 79, nBSV = 73 Total nSV = 79 Accuracy = 96% (96/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 42 nu = 0.690548 obj = -12.746167, rho = -0.114741 nSV = 70, nBSV = 67 Total nSV = 70 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 49 nu = 0.618700 obj = -14.642504, rho = -0.102643 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 96% (96/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 42 nu = 0.556581 obj = -16.869188, rho = -0.120317 nSV = 58, nBSV = 54 Total nSV = 58 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 34 nu = 0.505925 obj = -19.432431, rho = -0.182999 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 30 nu = 0.453363 obj = -22.395555, rho = -0.176609 nSV = 47, nBSV = 43 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 30 nu = 0.420000 obj = -25.827300, rho = -0.148459 nSV = 44, nBSV = 41 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 85 nu = 0.383752 obj = -29.321371, rho = -0.099611 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 96% (96/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 59 nu = 0.341926 obj = -33.389768, rho = -0.063013 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 50 nu = 0.302124 obj = -37.996109, rho = -0.018603 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 60 nu = 0.266708 obj = -43.376685, rho = 0.003910 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 59 nu = 0.241096 obj = -49.617600, rho = 0.016108 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 73 nu = 0.217917 obj = -56.784815, rho = -0.021229 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.194192 obj = -64.682223, rho = -0.001792 nSV = 25, nBSV = 15 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 68 nu = 0.174914 obj = -73.995873, rho = 0.000406 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*..* optimization finished, #iter = 358 nu = 0.156340 obj = -84.135737, rho = 0.017385 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.138062 obj = -96.329004, rho = 0.073492 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 49 nu = 0.929376 obj = -6.786938, rho = -0.031914 nSV = 95, nBSV = 92 Total nSV = 95 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 58 nu = 0.861819 obj = -7.858622, rho = -0.095588 nSV = 88, nBSV = 83 Total nSV = 88 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 48 nu = 0.793331 obj = -9.083553, rho = -0.104737 nSV = 81, nBSV = 77 Total nSV = 81 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 42 nu = 0.715556 obj = -10.444893, rho = -0.078577 nSV = 74, nBSV = 70 Total nSV = 74 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 37 nu = 0.648918 obj = -12.031422, rho = -0.069169 nSV = 67, nBSV = 64 Total nSV = 67 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 42 nu = 0.594705 obj = -13.797172, rho = -0.087574 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 30 nu = 0.546089 obj = -15.703794, rho = -0.099394 nSV = 56, nBSV = 54 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 48 nu = 0.486764 obj = -17.720571, rho = -0.113900 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 60 nu = 0.426860 obj = -20.016830, rho = -0.124342 nSV = 48, nBSV = 40 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 46 nu = 0.379672 obj = -22.661684, rho = -0.210838 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 35 nu = 0.340210 obj = -25.629244, rho = -0.157868 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 36 nu = 0.304604 obj = -28.919741, rho = -0.205873 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.274762 obj = -32.387872, rho = -0.183864 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 59 nu = 0.242217 obj = -36.028185, rho = -0.187181 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 69 nu = 0.209159 obj = -40.056199, rho = -0.181068 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 85 nu = 0.184030 obj = -44.538100, rho = -0.215136 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 98 nu = 0.161746 obj = -49.339595, rho = -0.276097 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 179 nu = 0.147825 obj = -53.930005, rho = -0.180144 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *..* optimization finished, #iter = 252 nu = 0.126204 obj = -58.118193, rho = -0.125529 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *..* optimization finished, #iter = 208 nu = 0.104928 obj = -62.568967, rho = -0.117935 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 48 nu = 0.960000 obj = -7.155308, rho = 0.146796 nSV = 96, nBSV = 96 Total nSV = 96 Accuracy = 96% (96/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 57 nu = 0.909103 obj = -8.319553, rho = 0.077516 nSV = 93, nBSV = 88 Total nSV = 93 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 47 nu = 0.836923 obj = -9.594955, rho = 0.038116 nSV = 85, nBSV = 82 Total nSV = 85 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 43 nu = 0.766155 obj = -11.021154, rho = 0.091607 nSV = 79, nBSV = 76 Total nSV = 79 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 51 nu = 0.694504 obj = -12.594021, rho = 0.102208 nSV = 72, nBSV = 68 Total nSV = 72 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 48 nu = 0.627275 obj = -14.310057, rho = 0.132211 nSV = 65, nBSV = 59 Total nSV = 65 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 40 nu = 0.561575 obj = -16.273603, rho = 0.165616 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 77 nu = 0.505713 obj = -18.434708, rho = 0.106839 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 45 nu = 0.450974 obj = -20.853456, rho = 0.094055 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 53 nu = 0.395216 obj = -23.575061, rho = 0.084331 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 66 nu = 0.355146 obj = -26.565685, rho = 0.145808 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 93 nu = 0.307961 obj = -30.016449, rho = 0.191646 nSV = 37, nBSV = 28 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 40 nu = 0.269821 obj = -34.207098, rho = 0.229346 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 42 nu = 0.242351 obj = -39.179675, rho = 0.228086 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 57 nu = 0.218562 obj = -44.739507, rho = 0.189904 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 55 nu = 0.197914 obj = -50.838703, rho = 0.192452 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 65 nu = 0.178643 obj = -57.262714, rho = 0.275496 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 89 nu = 0.161827 obj = -64.394396, rho = 0.264536 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 162 nu = 0.141437 obj = -71.823595, rho = 0.212346 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 187 nu = 0.124272 obj = -80.160252, rho = 0.159786 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification)
In [ ]:
import numpy as np
import numpy.matlib as matlib
from libsvm.svmutil import *
import matplotlib.pyplot as plt
def data(N,sigma):
w = np.ones(10)/np.sqrt(10)
w1 = [1., 1., 1., 1., 1., -1., -1., -1., -1., -1.]/np.sqrt(10)
w2 = [-1., -1., 0, 1., 1., -1., -1., 0, -1., -1.]/np.sqrt(8)
x = np.zeros((4,10))
x[1,:] = x[0,:] + sigma*w1
x[2,:] = x[0,:] + sigma*w2
x[3,:] = x[2,:] + sigma*w1
X1 = x + sigma*matlib.repmat(w,4,1)/2
X2 = x - sigma*matlib.repmat(w,4,1)/2
X1 = matlib.repmat(X1,2*N,1)
X2 = matlib.repmat(X2,2*N,1)
X = np.concatenate((X1, X2), axis=0)
Y = np.concatenate((np.ones(4*2*N), -np.ones(4*2*N)),axis=0)
Z = np.random.permutation(16*N)
Z = Z[:N]
X = X[Z,:]
X = X + 0.2*sigma*np.random.randn(N,10)
Y = Y[Z]
return X, Y
# Task 2a: Generating Parameter Values
lambda_values = np.logspace(-1, 1, 20) # Logarithmically spaced values between 0.01 and 10
# Initialize arrays to store errors
training_errors = []
test_errors = []
sigma = 1
# Task 2b-d: Training, Testing, and Repeating the Experiment
# num_iterations = 100
for i in range(num_iterations):
# Generate data
X_train, y_train = data(100,sigma)
X_test, y_test = data(1000, sigma)
for lam in lambda_values:
# Train SVM
svm_problem_setup = svm_problem(y_train.tolist(), X_train.tolist())
param = svm_parameter(f'-t 0 -c {lam}')
model = svm_train(svm_problem_setup, param)
# Predict on training and test data
i, train_accuracy, i = svm_predict(y_train.tolist(), X_train.tolist(), model)
i, test_accuracy, i = svm_predict(y_test.tolist(), X_test.tolist(), model)
# Calculate errors
training_errors.append(100 - train_accuracy[0]) # Convert to error percentage
test_errors.append(100 - test_accuracy[0]) # Convert to error percentage
# Task 2e: Averaging Errors and Plotting
training_errors = np.array(training_errors).reshape(num_iterations, -1)
test_errors = np.array(test_errors).reshape(num_iterations, -1)
avg_training_error = np.mean(training_errors, axis=0)
avg_test_error = np.mean(test_errors, axis=0)
lambda_values_log = np.log10(lambda_values)
# Plotting
plt.figure(figsize=(10, 6))
plt.plot(lambda_values_log, avg_training_error, label='R_empirical (Average Training Error)')
plt.plot(lambda_values_log, avg_test_error, label='R_actual (Average Test Error)')
plt.plot(lambda_values_log, avg_test_error - avg_training_error, label='R_structural (Difference)')
plt.xlabel('log(λ)')
plt.ylabel('Error (%)')
plt.title('Risks vs. λ (0.1,10)@ σ = 1')
plt.legend()
plt.show()
* optimization finished, #iter = 41 nu = 0.524674 obj = -3.426639, rho = 0.054033 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 52 nu = 0.456950 obj = -3.849325, rho = 0.089089 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 34 nu = 0.407930 obj = -4.317322, rho = 0.087179 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 38 nu = 0.360909 obj = -4.830806, rho = 0.080314 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 35 nu = 0.317275 obj = -5.406955, rho = 0.141655 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 76 nu = 0.275960 obj = -6.042330, rho = 0.192253 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 59 nu = 0.238280 obj = -6.786037, rho = 0.233015 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 38 nu = 0.210946 obj = -7.657470, rho = 0.273550 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 70 nu = 0.189372 obj = -8.584299, rho = 0.242646 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 28 nu = 0.169735 obj = -9.612962, rho = 0.354699 nSV = 20, nBSV = 15 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) *..* optimization finished, #iter = 214 nu = 0.148726 obj = -10.629356, rho = 0.398824 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 58 nu = 0.129161 obj = -11.793983, rho = 0.420145 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 95 nu = 0.119714 obj = -12.858513, rho = 0.558814 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) *.* optimization finished, #iter = 155 nu = 0.100362 obj = -13.801609, rho = 0.593642 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .* optimization finished, #iter = 172 nu = 0.085470 obj = -14.791812, rho = 0.627258 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) *.* optimization finished, #iter = 140 nu = 0.073765 obj = -15.729160, rho = 0.719111 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.060541 obj = -16.592606, rho = 0.735506 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) .* optimization finished, #iter = 158 nu = 0.049925 obj = -17.646933, rho = 0.744050 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.044589 obj = -18.514225, rho = 0.835648 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.4% (944/1000) (classification) .*.* optimization finished, #iter = 215 nu = 0.036532 obj = -18.880787, rho = 0.878620 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 35 nu = 0.633402 obj = -4.360823, rho = -0.165353 nSV = 64, nBSV = 62 Total nSV = 64 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 61 nu = 0.564777 obj = -4.970838, rho = -0.143532 nSV = 61, nBSV = 53 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 38 nu = 0.509844 obj = -5.682698, rho = -0.241055 nSV = 53, nBSV = 49 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 76 nu = 0.456039 obj = -6.473726, rho = -0.226976 nSV = 49, nBSV = 41 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 48 nu = 0.411579 obj = -7.366888, rho = -0.180442 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 72 nu = 0.364257 obj = -8.381903, rho = -0.170899 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.318032 obj = -9.597034, rho = -0.166548 nSV = 38, nBSV = 28 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.285009 obj = -11.091985, rho = -0.189695 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 55 nu = 0.259607 obj = -12.779173, rho = -0.255786 nSV = 30, nBSV = 21 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 41 nu = 0.229352 obj = -14.806849, rho = -0.226138 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 89 nu = 0.209512 obj = -17.254770, rho = -0.198102 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 86 nu = 0.198004 obj = -19.996378, rho = -0.038794 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.181564 obj = -22.929080, rho = 0.036565 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 174 nu = 0.164780 obj = -26.180854, rho = 0.068090 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..*....* optimization finished, #iter = 606 nu = 0.145199 obj = -29.746157, rho = 0.081874 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 141 nu = 0.129084 obj = -34.108557, rho = 0.062484 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 228 nu = 0.115398 obj = -39.205744, rho = 0.017936 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 223 nu = 0.103762 obj = -45.010564, rho = 0.035588 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) ...*.* optimization finished, #iter = 435 nu = 0.093642 obj = -51.917830, rho = 0.168192 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..*..* optimization finished, #iter = 470 nu = 0.082396 obj = -60.145401, rho = 0.167415 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 35 nu = 0.598373 obj = -4.032125, rho = -0.092021 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 37 nu = 0.536987 obj = -4.559551, rho = -0.153330 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 33 nu = 0.486807 obj = -5.126735, rho = -0.142706 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 65 nu = 0.423815 obj = -5.725633, rho = -0.131543 nSV = 47, nBSV = 39 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.377235 obj = -6.397743, rho = -0.122084 nSV = 41, nBSV = 33 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 60 nu = 0.326639 obj = -7.151575, rho = -0.103612 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.286058 obj = -8.015422, rho = -0.120340 nSV = 34, nBSV = 24 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 60 nu = 0.249183 obj = -9.000918, rho = -0.122002 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 54 nu = 0.222434 obj = -10.115382, rho = -0.135626 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.194896 obj = -11.326522, rho = -0.151782 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 204 nu = 0.168687 obj = -12.764837, rho = -0.150504 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 298 nu = 0.145566 obj = -14.525523, rho = -0.148360 nSV = 22, nBSV = 11 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 251 nu = 0.127867 obj = -16.682366, rho = -0.177614 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.113119 obj = -19.383068, rho = -0.191197 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 68 nu = 0.106589 obj = -22.544858, rho = -0.164963 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.097991 obj = -25.879296, rho = -0.135686 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 161 nu = 0.085309 obj = -29.871063, rho = -0.121708 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 188 nu = 0.078612 obj = -34.725907, rho = -0.095499 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 99 nu = 0.072496 obj = -40.182843, rho = -0.115764 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 211 nu = 0.068479 obj = -45.738429, rho = -0.296634 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 41 nu = 0.566794 obj = -3.789309, rho = -0.192724 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.507280 obj = -4.272957, rho = -0.202052 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.444653 obj = -4.820088, rho = -0.273180 nSV = 49, nBSV = 42 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.392741 obj = -5.439516, rho = -0.358957 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 32 nu = 0.354305 obj = -6.138155, rho = -0.367227 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 51 nu = 0.317615 obj = -6.880720, rho = -0.187618 nSV = 36, nBSV = 27 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 43 nu = 0.274601 obj = -7.701378, rho = -0.216300 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 31 nu = 0.243142 obj = -8.617110, rho = -0.253439 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 61 nu = 0.213671 obj = -9.651309, rho = -0.355843 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 148 nu = 0.182592 obj = -10.857688, rho = -0.350807 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 52 nu = 0.162059 obj = -12.329893, rho = -0.346352 nSV = 19, nBSV = 14 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 77 nu = 0.141556 obj = -13.995748, rho = -0.344140 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 49 nu = 0.128008 obj = -15.968067, rho = -0.396838 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 66 nu = 0.116226 obj = -18.108468, rho = -0.445162 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 159 nu = 0.103635 obj = -20.416922, rho = -0.375920 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 273 nu = 0.089952 obj = -23.025799, rho = -0.343108 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.079335 obj = -26.146787, rho = -0.319371 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 143 nu = 0.071995 obj = -29.693867, rho = -0.243584 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 161 nu = 0.065659 obj = -33.399576, rho = -0.200385 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 156 nu = 0.059795 obj = -36.875643, rho = -0.122925 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 37 nu = 0.575878 obj = -4.030179, rho = -0.117439 nSV = 59, nBSV = 56 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 38 nu = 0.525038 obj = -4.624628, rho = -0.051531 nSV = 54, nBSV = 51 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 71 nu = 0.474760 obj = -5.275695, rho = -0.113356 nSV = 49, nBSV = 46 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 38 nu = 0.425649 obj = -6.014769, rho = -0.158107 nSV = 45, nBSV = 41 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 35 nu = 0.381637 obj = -6.817999, rho = -0.138205 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 42 nu = 0.338720 obj = -7.754752, rho = -0.091198 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 31 nu = 0.307727 obj = -8.810544, rho = -0.148431 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 58 nu = 0.279381 obj = -9.912478, rho = -0.134728 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.242426 obj = -11.095602, rho = -0.140498 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 74 nu = 0.212308 obj = -12.483249, rho = -0.132954 nSV = 27, nBSV = 17 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.185480 obj = -14.099271, rho = -0.125549 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 143 nu = 0.166311 obj = -15.963932, rho = -0.121195 nSV = 18, nBSV = 13 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 235 nu = 0.147127 obj = -17.967265, rho = -0.137190 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*...* optimization finished, #iter = 461 nu = 0.127471 obj = -20.387112, rho = -0.098693 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..* optimization finished, #iter = 266 nu = 0.113791 obj = -23.240545, rho = -0.121412 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.102201 obj = -26.475022, rho = -0.128762 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 153 nu = 0.092700 obj = -30.038788, rho = -0.106127 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.081975 obj = -34.104281, rho = -0.097644 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 164 nu = 0.074390 obj = -38.344603, rho = -0.133895 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 95 nu = 0.064609 obj = -43.235222, rho = -0.104981 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 51 nu = 0.537970 obj = -3.735219, rho = -0.361334 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 31 nu = 0.480000 obj = -4.289577, rho = -0.362031 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 39 nu = 0.439936 obj = -4.905142, rho = -0.293943 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 43 nu = 0.393548 obj = -5.604305, rho = -0.276642 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 60 nu = 0.350697 obj = -6.406144, rho = -0.283145 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 53 nu = 0.316440 obj = -7.302437, rho = -0.376562 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 34 nu = 0.287622 obj = -8.309957, rho = -0.397898 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.258115 obj = -9.379955, rho = -0.401159 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 55 nu = 0.231509 obj = -10.566708, rho = -0.400909 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 83 nu = 0.206735 obj = -11.829984, rho = -0.425726 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 55 nu = 0.184273 obj = -13.141870, rho = -0.441441 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 137 nu = 0.161060 obj = -14.482036, rho = -0.426516 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 192 nu = 0.139557 obj = -15.986074, rho = -0.431313 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 148 nu = 0.118947 obj = -17.657866, rho = -0.446923 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 88 nu = 0.101214 obj = -19.672253, rho = -0.480158 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 92 nu = 0.093790 obj = -21.900556, rho = -0.545001 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 68 nu = 0.081872 obj = -23.832461, rho = -0.718284 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 71 nu = 0.070905 obj = -25.797018, rho = -0.930667 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 94 nu = 0.063833 obj = -27.447009, rho = -1.232219 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.052576 obj = -28.431062, rho = -1.371294 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 46 nu = 0.510124 obj = -3.460520, rho = -0.158433 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 42 nu = 0.458232 obj = -3.926896, rho = -0.206117 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 44 nu = 0.406245 obj = -4.444559, rho = -0.251948 nSV = 43, nBSV = 39 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 41 nu = 0.363767 obj = -5.011690, rho = -0.270522 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 43 nu = 0.322376 obj = -5.643622, rho = -0.330951 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 50 nu = 0.282803 obj = -6.368024, rho = -0.399451 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 50 nu = 0.250664 obj = -7.194812, rho = -0.378748 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.222664 obj = -8.124402, rho = -0.346695 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 33 nu = 0.200000 obj = -9.199696, rho = -0.291748 nSV = 22, nBSV = 18 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 30 nu = 0.175791 obj = -10.346148, rho = -0.267583 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 44 nu = 0.157823 obj = -11.606999, rho = -0.301152 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 63 nu = 0.137648 obj = -13.002738, rho = -0.316721 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 68 nu = 0.121924 obj = -14.630057, rho = -0.297584 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 77 nu = 0.106996 obj = -16.434666, rho = -0.228643 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.095566 obj = -18.414425, rho = -0.153021 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 287 nu = 0.085782 obj = -20.389426, rho = -0.161995 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 159 nu = 0.072363 obj = -22.643929, rho = -0.184102 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 197 nu = 0.062785 obj = -25.335368, rho = -0.191745 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..*.* optimization finished, #iter = 350 nu = 0.054256 obj = -28.521349, rho = -0.205003 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..*.* optimization finished, #iter = 338 nu = 0.046650 obj = -32.451961, rho = -0.208744 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 36 nu = 0.521721 obj = -3.518682, rho = -0.211027 nSV = 54, nBSV = 51 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 43 nu = 0.470358 obj = -3.966321, rho = -0.189445 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 37 nu = 0.416749 obj = -4.466159, rho = -0.144464 nSV = 43, nBSV = 39 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.368761 obj = -5.008778, rho = -0.132146 nSV = 40, nBSV = 30 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 81 nu = 0.320080 obj = -5.639817, rho = -0.144975 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 36 nu = 0.289020 obj = -6.381834, rho = -0.099651 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 37 nu = 0.257277 obj = -7.153107, rho = -0.055592 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 40 nu = 0.228101 obj = -7.973186, rho = 0.030352 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.200846 obj = -8.837806, rho = 0.009743 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 58 nu = 0.174889 obj = -9.816861, rho = 0.010848 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.154826 obj = -10.796379, rho = 0.100338 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 82 nu = 0.132128 obj = -11.880092, rho = 0.071298 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 121 nu = 0.120463 obj = -12.973109, rho = -0.013815 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 163 nu = 0.101993 obj = -13.970643, rho = -0.042453 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 269 nu = 0.084910 obj = -15.051270, rho = -0.043099 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 165 nu = 0.072504 obj = -16.296105, rho = -0.075885 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.062400 obj = -17.538149, rho = -0.155733 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 184 nu = 0.054004 obj = -18.656309, rho = -0.275186 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) ..*.* optimization finished, #iter = 394 nu = 0.044720 obj = -19.655164, rho = -0.349115 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ...........*..* optimization finished, #iter = 1371 nu = 0.036669 obj = -20.710429, rho = -0.388398 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 43 nu = 0.561713 obj = -3.723660, rho = -0.187662 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.497612 obj = -4.197891, rho = -0.258976 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 29 nu = 0.447002 obj = -4.706681, rho = -0.217578 nSV = 47, nBSV = 43 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 42 nu = 0.394534 obj = -5.249465, rho = -0.238001 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 49 nu = 0.350568 obj = -5.842852, rho = -0.233623 nSV = 38, nBSV = 30 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 71 nu = 0.302765 obj = -6.482313, rho = -0.223827 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 69 nu = 0.267676 obj = -7.153977, rho = -0.183113 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 70 nu = 0.232470 obj = -7.884295, rho = -0.214153 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 199 nu = 0.196900 obj = -8.680628, rho = -0.236592 nSV = 26, nBSV = 15 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 164 nu = 0.167765 obj = -9.667779, rho = -0.217135 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 158 nu = 0.146024 obj = -10.848209, rho = -0.246008 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.128041 obj = -12.205752, rho = -0.276023 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.115270 obj = -13.690133, rho = -0.319183 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *..* optimization finished, #iter = 261 nu = 0.101321 obj = -15.221109, rho = -0.340112 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*.* optimization finished, #iter = 300 nu = 0.086254 obj = -17.064210, rho = -0.353862 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 178 nu = 0.074360 obj = -19.333131, rho = -0.365922 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.067216 obj = -22.073035, rho = -0.475954 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 182 nu = 0.058283 obj = -25.246917, rho = -0.461754 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 86 nu = 0.051413 obj = -29.190959, rho = -0.435765 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.046904 obj = -33.964500, rho = -0.398524 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 39 nu = 0.586266 obj = -3.830976, rho = -0.189876 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 33 nu = 0.521502 obj = -4.279663, rho = -0.181914 nSV = 54, nBSV = 51 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 35 nu = 0.464836 obj = -4.727898, rho = -0.246097 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 50 nu = 0.402976 obj = -5.201486, rho = -0.297189 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 70 nu = 0.344290 obj = -5.744532, rho = -0.292096 nSV = 38, nBSV = 29 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 51 nu = 0.297088 obj = -6.374794, rho = -0.277629 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 79 nu = 0.262107 obj = -7.066334, rho = -0.385090 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 49 nu = 0.225587 obj = -7.842517, rho = -0.401724 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 69 nu = 0.199664 obj = -8.650867, rho = -0.462280 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 48 nu = 0.170355 obj = -9.540720, rho = -0.487140 nSV = 20, nBSV = 15 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.149527 obj = -10.509269, rho = -0.584479 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 66 nu = 0.128067 obj = -11.579584, rho = -0.528775 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 83 nu = 0.109093 obj = -12.857949, rho = -0.537313 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 38 nu = 0.098765 obj = -14.227360, rho = -0.706868 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 91 nu = 0.091462 obj = -15.270496, rho = -0.796378 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *..* optimization finished, #iter = 210 nu = 0.075527 obj = -16.079444, rho = -0.843790 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 66 nu = 0.063171 obj = -16.921187, rho = -0.877818 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 52 nu = 0.053327 obj = -17.639049, rho = -1.002101 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 69 nu = 0.044910 obj = -18.037050, rho = -1.035908 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 55 nu = 0.036124 obj = -18.064616, rho = -1.053589 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 51 nu = 0.482696 obj = -3.249242, rho = -0.080972 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 40 nu = 0.433539 obj = -3.674639, rho = -0.043805 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 45 nu = 0.380318 obj = -4.146110, rho = -0.059503 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 53 nu = 0.340000 obj = -4.693275, rho = -0.041651 nSV = 35, nBSV = 32 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 54 nu = 0.300718 obj = -5.311439, rho = -0.004149 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.265048 obj = -6.003667, rho = 0.042494 nSV = 30, nBSV = 21 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 95 nu = 0.233646 obj = -6.829234, rho = 0.073272 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 98 nu = 0.204146 obj = -7.812170, rho = 0.105565 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 37 nu = 0.184917 obj = -8.991004, rho = 0.105567 nSV = 20, nBSV = 17 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 40 nu = 0.167956 obj = -10.281040, rho = 0.035187 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 44 nu = 0.153095 obj = -11.736873, rho = 0.141197 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 74 nu = 0.137421 obj = -13.311555, rho = 0.250557 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 71 nu = 0.122412 obj = -15.060159, rho = 0.374005 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 130 nu = 0.111918 obj = -16.978900, rho = 0.527250 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 207 nu = 0.099777 obj = -18.941392, rho = 0.610899 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.088430 obj = -20.895442, rho = 0.647456 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 139 nu = 0.077921 obj = -22.899415, rho = 0.749236 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 159 nu = 0.068693 obj = -24.865295, rho = 0.876593 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .*...* optimization finished, #iter = 443 nu = 0.060414 obj = -26.395875, rho = 1.007737 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ..*..* optimization finished, #iter = 474 nu = 0.051556 obj = -27.507338, rho = 1.041088 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 63 nu = 0.556339 obj = -3.735080, rho = -0.252920 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 49 nu = 0.490910 obj = -4.228337, rho = -0.249165 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.440000 obj = -4.790140, rho = -0.263084 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 30 nu = 0.391977 obj = -5.428069, rho = -0.283223 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 57 nu = 0.347966 obj = -6.130474, rho = -0.274531 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 82 nu = 0.309800 obj = -6.902157, rho = -0.288083 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 67 nu = 0.278141 obj = -7.752995, rho = -0.301656 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *..* optimization finished, #iter = 228 nu = 0.240954 obj = -8.680168, rho = -0.298583 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 52 nu = 0.211821 obj = -9.778135, rho = -0.318076 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.186807 obj = -10.993188, rho = -0.347483 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 58 nu = 0.166579 obj = -12.400472, rho = -0.298970 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 52 nu = 0.147104 obj = -13.890251, rho = -0.223916 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 42 nu = 0.129303 obj = -15.627556, rho = -0.210266 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.115710 obj = -17.520788, rho = -0.162968 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.104615 obj = -19.331630, rho = -0.065729 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 69 nu = 0.091049 obj = -21.275875, rho = -0.064203 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 67 nu = 0.083837 obj = -22.902507, rho = -0.088198 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 86 nu = 0.073182 obj = -23.992744, rho = -0.042683 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 181 nu = 0.061576 obj = -24.162412, rho = -0.031387 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 181 nu = 0.048322 obj = -24.162412, rho = -0.031387 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 52 nu = 0.571414 obj = -3.956764, rho = -0.078597 nSV = 60, nBSV = 52 Total nSV = 60 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 45 nu = 0.511820 obj = -4.527600, rho = -0.035943 nSV = 53, nBSV = 50 Total nSV = 53 Accuracy = 97% (97/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 41 nu = 0.460282 obj = -5.186603, rho = -0.016345 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 83 nu = 0.411629 obj = -5.934340, rho = 0.066941 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 41 nu = 0.371331 obj = -6.786227, rho = 0.061695 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 46 nu = 0.331579 obj = -7.774679, rho = 0.126535 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 51 nu = 0.299993 obj = -8.896941, rho = 0.156147 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 77 nu = 0.270896 obj = -10.139447, rho = 0.123108 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 51 nu = 0.241791 obj = -11.582595, rho = 0.112959 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 72 nu = 0.215464 obj = -13.209649, rho = 0.192115 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 75 nu = 0.191248 obj = -15.156014, rho = 0.160453 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 65 nu = 0.177094 obj = -17.378405, rho = 0.035688 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.162820 obj = -19.649236, rho = 0.068646 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 154 nu = 0.142595 obj = -22.113131, rho = 0.069392 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.131814 obj = -24.826251, rho = 0.059653 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.116576 obj = -27.201010, rho = -0.025524 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 182 nu = 0.100031 obj = -29.836906, rho = -0.011843 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) ...*..* optimization finished, #iter = 559 nu = 0.086042 obj = -32.667236, rho = 0.059437 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) ...*.* optimization finished, #iter = 474 nu = 0.074294 obj = -35.878135, rho = 0.096641 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..*.* optimization finished, #iter = 365 nu = 0.065896 obj = -39.002975, rho = 0.224457 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 47 nu = 0.571436 obj = -3.852845, rho = -0.029186 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 62 nu = 0.503579 obj = -4.375192, rho = -0.051331 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 55 nu = 0.456119 obj = -4.962754, rho = -0.080345 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 61 nu = 0.402311 obj = -5.612288, rho = -0.096946 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 36 nu = 0.356986 obj = -6.360708, rho = -0.074943 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 55 nu = 0.314014 obj = -7.221383, rho = -0.119578 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 36 nu = 0.282377 obj = -8.221105, rho = -0.163225 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 38 nu = 0.251566 obj = -9.342770, rho = -0.194098 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 51 nu = 0.227287 obj = -10.584043, rho = -0.173616 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 68 nu = 0.199137 obj = -12.001682, rho = -0.169821 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 45 nu = 0.177082 obj = -13.666647, rho = -0.113665 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 31 nu = 0.159287 obj = -15.523403, rho = -0.061452 nSV = 18, nBSV = 14 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.148106 obj = -17.443284, rho = -0.253001 nSV = 17, nBSV = 13 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 38 nu = 0.129980 obj = -19.402611, rho = -0.214165 nSV = 15, nBSV = 10 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 0.118999 obj = -21.412907, rho = -0.087743 nSV = 13, nBSV = 8 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 75 nu = 0.106263 obj = -22.966340, rho = -0.042066 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 86 nu = 0.091726 obj = -24.136259, rho = -0.048349 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.076318 obj = -24.889978, rho = -0.014587 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 99 nu = 0.061324 obj = -25.642946, rho = -0.007399 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 85 nu = 0.050290 obj = -26.141843, rho = -0.035933 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.591900 obj = -4.019346, rho = -0.074871 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 42 nu = 0.535355 obj = -4.551700, rho = -0.173094 nSV = 56, nBSV = 50 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 45 nu = 0.471642 obj = -5.146496, rho = -0.189977 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 93 nu = 0.423468 obj = -5.792129, rho = -0.233424 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 99 nu = 0.374255 obj = -6.520496, rho = -0.225404 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 75 nu = 0.328185 obj = -7.333253, rho = -0.233066 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 59 nu = 0.292211 obj = -8.265603, rho = -0.197860 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 54 nu = 0.254108 obj = -9.337681, rho = -0.229209 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 45 nu = 0.230533 obj = -10.563070, rho = -0.273925 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 38 nu = 0.209702 obj = -11.813850, rho = -0.163491 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*..* optimization finished, #iter = 334 nu = 0.189240 obj = -12.927778, rho = -0.266326 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *..* optimization finished, #iter = 213 nu = 0.162339 obj = -14.035096, rho = -0.263160 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 241 nu = 0.140654 obj = -15.190975, rho = -0.330936 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 77 nu = 0.118409 obj = -16.394422, rho = -0.300660 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.101026 obj = -17.594645, rho = -0.253241 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.090385 obj = -18.628902, rho = -0.254357 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 169 nu = 0.074954 obj = -19.264315, rho = -0.205588 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 199 nu = 0.060940 obj = -19.759575, rho = -0.166450 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 80 nu = 0.049635 obj = -20.195146, rho = -0.092645 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*..* optimization finished, #iter = 356 nu = 0.040221 obj = -20.428789, rho = 0.027311 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 77 nu = 0.571366 obj = -3.860609, rho = -0.040854 nSV = 61, nBSV = 53 Total nSV = 61 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 55 nu = 0.502266 obj = -4.395034, rho = -0.080784 nSV = 55, nBSV = 47 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 48 nu = 0.443162 obj = -5.026150, rho = -0.104304 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 60 nu = 0.399774 obj = -5.757485, rho = -0.065221 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 61 nu = 0.356148 obj = -6.623925, rho = -0.045589 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.322897 obj = -7.613228, rho = -0.099796 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 31 nu = 0.290594 obj = -8.762659, rho = -0.125238 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 27 nu = 0.262989 obj = -10.060247, rho = -0.233232 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 42 nu = 0.244870 obj = -11.448611, rho = -0.271769 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.217843 obj = -12.957489, rho = -0.222891 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 65 nu = 0.191741 obj = -14.725011, rho = -0.162591 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 52 nu = 0.173815 obj = -16.682589, rho = -0.026270 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 295 nu = 0.153943 obj = -18.788837, rho = 0.027522 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 164 nu = 0.135395 obj = -21.253047, rho = 0.001790 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 174 nu = 0.121140 obj = -24.028170, rho = 0.011729 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 154 nu = 0.109478 obj = -27.094695, rho = 0.015113 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 139 nu = 0.098459 obj = -30.201165, rho = 0.093842 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 160 nu = 0.090122 obj = -33.069347, rho = 0.304451 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 158 nu = 0.083157 obj = -35.049824, rho = 0.493575 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ..* optimization finished, #iter = 263 nu = 0.069762 obj = -35.911127, rho = 0.584372 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 35 nu = 0.513706 obj = -3.367647, rho = -0.220336 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 35 nu = 0.454829 obj = -3.773906, rho = -0.210820 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 44 nu = 0.401113 obj = -4.227294, rho = -0.216282 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 38 nu = 0.353873 obj = -4.696193, rho = -0.149376 nSV = 38, nBSV = 33 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 48 nu = 0.308642 obj = -5.220284, rho = -0.125456 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 29 nu = 0.273253 obj = -5.800280, rho = -0.062483 nSV = 29, nBSV = 25 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 115 nu = 0.244839 obj = -6.362576, rho = -0.139233 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) *..* optimization finished, #iter = 278 nu = 0.206955 obj = -6.959219, rho = -0.159357 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) .* optimization finished, #iter = 182 nu = 0.177075 obj = -7.614671, rho = -0.178035 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.149286 obj = -8.409475, rho = -0.189763 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 62 nu = 0.131295 obj = -9.319925, rho = -0.206073 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 45 nu = 0.112440 obj = -10.312242, rho = -0.219317 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 41 nu = 0.102128 obj = -11.365280, rho = -0.093235 nSV = 13, nBSV = 8 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 60 nu = 0.088710 obj = -12.307407, rho = -0.099687 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 65 nu = 0.074438 obj = -13.303853, rho = -0.153067 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 61 nu = 0.061688 obj = -14.522237, rho = -0.158035 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 94 nu = 0.051664 obj = -16.067670, rho = -0.162855 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 68 nu = 0.043963 obj = -18.023663, rho = -0.195936 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 77 nu = 0.038227 obj = -20.420394, rho = -0.298256 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 81 nu = 0.033738 obj = -23.290008, rho = -0.423681 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 44 nu = 0.595466 obj = -4.139774, rho = -0.188766 nSV = 63, nBSV = 58 Total nSV = 63 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 86 nu = 0.531242 obj = -4.741601, rho = -0.171155 nSV = 57, nBSV = 50 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 38 nu = 0.482208 obj = -5.438919, rho = -0.116372 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 51 nu = 0.439475 obj = -6.203714, rho = -0.073137 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 47 nu = 0.397290 obj = -7.042804, rho = -0.125804 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 73 nu = 0.353672 obj = -7.964510, rho = -0.097925 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 97 nu = 0.309261 obj = -9.039634, rho = -0.056092 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 54 nu = 0.277106 obj = -10.277949, rho = -0.024254 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 52 nu = 0.248047 obj = -11.640421, rho = -0.087403 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.220205 obj = -13.190076, rho = -0.163223 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 57 nu = 0.195612 obj = -14.991465, rho = -0.134267 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 52 nu = 0.175524 obj = -17.006784, rho = -0.194545 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 88 nu = 0.158453 obj = -19.201736, rho = -0.199424 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.137567 obj = -21.701352, rho = -0.258440 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.123771 obj = -24.563454, rho = -0.329970 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 159 nu = 0.109265 obj = -27.760475, rho = -0.344427 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 155 nu = 0.096331 obj = -31.465120, rho = -0.378593 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 283 nu = 0.083591 obj = -35.845802, rho = -0.380035 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) ...*.* optimization finished, #iter = 496 nu = 0.074433 obj = -41.239890, rho = -0.367242 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) ...*.....* optimization finished, #iter = 847 nu = 0.065436 obj = -47.819337, rho = -0.342692 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 62 nu = 0.570256 obj = -3.871646, rho = -0.205596 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 57 nu = 0.511475 obj = -4.390508, rho = -0.164070 nSV = 55, nBSV = 48 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 31 nu = 0.457141 obj = -4.972140, rho = -0.171807 nSV = 48, nBSV = 44 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 33 nu = 0.412476 obj = -5.594681, rho = -0.145929 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 52 nu = 0.365639 obj = -6.249887, rho = -0.118448 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 74 nu = 0.321382 obj = -6.977013, rho = -0.107921 nSV = 37, nBSV = 28 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 35 nu = 0.280408 obj = -7.815320, rho = -0.153136 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 38 nu = 0.245588 obj = -8.748292, rho = -0.146197 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 55 nu = 0.221212 obj = -9.735551, rho = -0.256815 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 57 nu = 0.193350 obj = -10.714074, rho = -0.345094 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 46 nu = 0.172400 obj = -11.773077, rho = -0.511636 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.148744 obj = -12.687538, rho = -0.628362 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 94 nu = 0.128429 obj = -13.683355, rho = -0.583334 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .**.* optimization finished, #iter = 181 nu = 0.109217 obj = -14.601121, rho = -0.443020 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*..* optimization finished, #iter = 309 nu = 0.090163 obj = -15.519234, rho = -0.399216 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.075502 obj = -16.619400, rho = -0.374297 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 81 nu = 0.064213 obj = -17.649722, rho = -0.363907 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.053262 obj = -18.739439, rho = -0.422798 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.045561 obj = -19.816752, rho = -0.566351 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.040715 obj = -20.364262, rho = -0.540058 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.579055 obj = -4.040271, rho = -0.190533 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 31 nu = 0.521125 obj = -4.644676, rho = -0.217387 nSV = 54, nBSV = 52 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 35 nu = 0.468876 obj = -5.324616, rho = -0.130086 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 39 nu = 0.425920 obj = -6.112236, rho = -0.091832 nSV = 45, nBSV = 41 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 26 nu = 0.392232 obj = -6.975671, rho = -0.065544 nSV = 40, nBSV = 38 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.352165 obj = -7.882198, rho = -0.129523 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 62 nu = 0.312599 obj = -8.881185, rho = -0.124020 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.281605 obj = -9.995281, rho = -0.243765 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 185 nu = 0.245192 obj = -11.210123, rho = -0.269863 nSV = 29, nBSV = 19 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 58 nu = 0.214850 obj = -12.627529, rho = -0.303628 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 74 nu = 0.188677 obj = -14.254101, rho = -0.326970 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.169706 obj = -16.021650, rho = -0.416062 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.150628 obj = -17.994292, rho = -0.294981 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.130330 obj = -20.197264, rho = -0.301549 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.113550 obj = -22.844957, rho = -0.235833 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.102484 obj = -25.890985, rho = -0.199223 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.094185 obj = -28.941920, rho = -0.216798 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.082774 obj = -32.050130, rho = -0.401115 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 79 nu = 0.073988 obj = -35.123301, rho = -0.481884 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 238 nu = 0.066218 obj = -37.936228, rho = -0.558699 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 42 nu = 0.640000 obj = -4.419598, rho = -0.398233 nSV = 65, nBSV = 60 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 42 nu = 0.584852 obj = -5.027928, rho = -0.309414 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 57 nu = 0.516059 obj = -5.714349, rho = -0.314487 nSV = 55, nBSV = 48 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 57 nu = 0.467221 obj = -6.472029, rho = -0.236096 nSV = 51, nBSV = 44 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 156 nu = 0.416000 obj = -7.293300, rho = -0.251501 nSV = 46, nBSV = 37 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 169 nu = 0.362119 obj = -8.270901, rho = -0.227260 nSV = 41, nBSV = 32 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 34 nu = 0.328817 obj = -9.393734, rho = -0.186704 nSV = 34, nBSV = 30 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 172 nu = 0.295811 obj = -10.502192, rho = -0.119844 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 43 nu = 0.260000 obj = -11.800914, rho = -0.108099 nSV = 28, nBSV = 24 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 46 nu = 0.228341 obj = -13.151759, rho = -0.066604 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.200621 obj = -14.691718, rho = -0.082567 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.175672 obj = -16.427072, rho = -0.140019 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *..* optimization finished, #iter = 241 nu = 0.153700 obj = -18.353417, rho = -0.228788 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.131523 obj = -20.678307, rho = -0.205470 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 98 nu = 0.115698 obj = -23.520685, rho = -0.177902 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.105677 obj = -26.624106, rho = -0.104963 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 120 nu = 0.095589 obj = -29.777695, rho = -0.215334 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 268 nu = 0.082876 obj = -33.221520, rho = -0.236021 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 177 nu = 0.071544 obj = -37.328325, rho = -0.180646 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.065291 obj = -42.021609, rho = -0.134583 nSV = 10, nBSV = 4 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 42 nu = 0.569620 obj = -3.945566, rho = -0.241220 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 97% (97/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 64 nu = 0.504121 obj = -4.527723, rho = -0.226718 nSV = 54, nBSV = 47 Total nSV = 54 Accuracy = 97% (97/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 58 nu = 0.454468 obj = -5.206804, rho = -0.185275 nSV = 51, nBSV = 43 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 54 nu = 0.413640 obj = -5.988898, rho = -0.167277 nSV = 46, nBSV = 37 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 55 nu = 0.378309 obj = -6.873615, rho = -0.074948 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 42 nu = 0.342652 obj = -7.848133, rho = -0.047457 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 47 nu = 0.312313 obj = -8.883080, rho = -0.007395 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 52 nu = 0.277644 obj = -9.996477, rho = 0.022119 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.246744 obj = -11.220956, rho = 0.092590 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 94 nu = 0.211392 obj = -12.650632, rho = 0.104022 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 69 nu = 0.189695 obj = -14.385338, rho = 0.063487 nSV = 22, nBSV = 17 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 166 nu = 0.167275 obj = -16.278893, rho = 0.065872 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 132 nu = 0.151508 obj = -18.431394, rho = 0.024802 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 56 nu = 0.132857 obj = -20.741107, rho = 0.039938 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 146 nu = 0.119477 obj = -23.362398, rho = 0.146521 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 57 nu = 0.106833 obj = -26.248065, rho = 0.158620 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 77 nu = 0.095850 obj = -29.180541, rho = 0.073370 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.085377 obj = -31.990452, rho = 0.005234 nSV = 11, nBSV = 5 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 174 nu = 0.075244 obj = -34.648338, rho = -0.023465 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 197 nu = 0.065048 obj = -36.893562, rho = 0.015959 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 58 nu = 0.620826 obj = -4.448658, rho = -0.328780 nSV = 65, nBSV = 60 Total nSV = 65 Accuracy = 93% (93/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 52 nu = 0.553834 obj = -5.164156, rho = -0.349318 nSV = 60, nBSV = 53 Total nSV = 60 Accuracy = 92% (92/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 49 nu = 0.509251 obj = -6.012875, rho = -0.329667 nSV = 55, nBSV = 48 Total nSV = 55 Accuracy = 93% (93/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 50 nu = 0.466959 obj = -6.986042, rho = -0.306421 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 95% (95/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 0.420431 obj = -8.127224, rho = -0.315835 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 94% (94/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 68 nu = 0.384309 obj = -9.476096, rho = -0.266281 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 94% (94/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 47 nu = 0.343689 obj = -11.108765, rho = -0.245696 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 94% (94/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.321379 obj = -13.051660, rho = -0.139385 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 96% (96/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 57 nu = 0.295852 obj = -15.296343, rho = -0.102811 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 96% (96/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 38 nu = 0.267938 obj = -17.976545, rho = -0.104221 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 96% (96/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 67 nu = 0.242981 obj = -21.289453, rho = -0.149794 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 96% (96/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 98 nu = 0.227792 obj = -25.261348, rho = -0.152259 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 96% (96/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 54 nu = 0.214974 obj = -29.891634, rho = -0.122996 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 96% (96/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 92 nu = 0.199818 obj = -35.134675, rho = -0.058941 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 185 nu = 0.182788 obj = -41.411468, rho = 0.044225 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 96% (96/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 123 nu = 0.169453 obj = -48.904976, rho = 0.132306 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 226 nu = 0.154415 obj = -57.955654, rho = 0.108410 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 172 nu = 0.142765 obj = -69.050914, rho = 0.103693 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 181 nu = 0.136059 obj = -82.195169, rho = 0.144214 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 238 nu = 0.129811 obj = -96.694463, rho = 0.219251 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 40 nu = 0.610520 obj = -4.180393, rho = -0.234420 nSV = 63, nBSV = 57 Total nSV = 63 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 53 nu = 0.551551 obj = -4.754736, rho = -0.180716 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.490079 obj = -5.399508, rho = -0.220503 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 65 nu = 0.435685 obj = -6.122962, rho = -0.251716 nSV = 48, nBSV = 40 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 55 nu = 0.386535 obj = -6.973177, rho = -0.230206 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 65 nu = 0.339875 obj = -7.964077, rho = -0.219153 nSV = 39, nBSV = 30 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 71 nu = 0.303427 obj = -9.157799, rho = -0.174248 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 60 nu = 0.270573 obj = -10.556295, rho = -0.153979 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 66 nu = 0.251969 obj = -12.139463, rho = -0.089386 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 186 nu = 0.225882 obj = -13.937674, rho = -0.151718 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 71 nu = 0.205381 obj = -15.955341, rho = -0.099403 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 69 nu = 0.187333 obj = -18.103563, rho = -0.002746 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..* optimization finished, #iter = 289 nu = 0.172107 obj = -20.295672, rho = -0.177493 nSV = 21, nBSV = 10 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *..........* optimization finished, #iter = 1045 nu = 0.148591 obj = -22.739326, rho = -0.195155 nSV = 20, nBSV = 9 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 259 nu = 0.132897 obj = -25.411280, rho = -0.285508 nSV = 19, nBSV = 8 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.116723 obj = -28.407609, rho = -0.381318 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.105623 obj = -31.335286, rho = -0.537082 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 151 nu = 0.092881 obj = -34.041398, rho = -0.553066 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) *.* optimization finished, #iter = 162 nu = 0.081729 obj = -36.363695, rho = -0.581984 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) ..*..* optimization finished, #iter = 497 nu = 0.070274 obj = -38.230077, rho = -0.718323 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 46 nu = 0.627730 obj = -4.285745, rho = -0.184683 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 96 nu = 0.561850 obj = -4.872385, rho = -0.213064 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 69 nu = 0.497220 obj = -5.552185, rho = -0.195401 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 59 nu = 0.449285 obj = -6.330133, rho = -0.190399 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 78 nu = 0.402301 obj = -7.174847, rho = -0.199190 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 83 nu = 0.358569 obj = -8.125282, rho = -0.198849 nSV = 41, nBSV = 33 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.320856 obj = -9.205036, rho = -0.180032 nSV = 37, nBSV = 28 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 31 nu = 0.295526 obj = -10.361671, rho = -0.154966 nSV = 31, nBSV = 27 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 59 nu = 0.262920 obj = -11.444274, rho = -0.144064 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 74 nu = 0.230446 obj = -12.583781, rho = -0.160734 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 196 nu = 0.197380 obj = -13.782636, rho = -0.192511 nSV = 25, nBSV = 15 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.168876 obj = -15.141008, rho = -0.218992 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 183 nu = 0.149786 obj = -16.546627, rho = -0.210213 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.128647 obj = -17.979016, rho = -0.200221 nSV = 18, nBSV = 7 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..*..* optimization finished, #iter = 479 nu = 0.109258 obj = -19.447840, rho = -0.120874 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 180 nu = 0.091901 obj = -21.104257, rho = -0.108783 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.079354 obj = -22.932765, rho = -0.211711 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 172 nu = 0.070066 obj = -24.544714, rho = -0.307268 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 181 nu = 0.060136 obj = -25.862608, rho = -0.499305 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 190 nu = 0.048881 obj = -26.998822, rho = -0.531434 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 40 nu = 0.629807 obj = -4.442715, rho = -0.098860 nSV = 66, nBSV = 62 Total nSV = 66 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 53 nu = 0.576408 obj = -5.086476, rho = -0.037182 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 68 nu = 0.520682 obj = -5.820456, rho = 0.004422 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 72 nu = 0.466749 obj = -6.645787, rho = -0.025632 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 43 nu = 0.414115 obj = -7.603590, rho = -0.034430 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 97% (97/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 61 nu = 0.376930 obj = -8.696217, rho = 0.015002 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 58 nu = 0.336640 obj = -9.929867, rho = 0.006857 nSV = 38, nBSV = 30 Total nSV = 38 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 45 nu = 0.303226 obj = -11.331468, rho = 0.002791 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 42 nu = 0.272287 obj = -12.935228, rho = -0.037734 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 47 nu = 0.253181 obj = -14.648026, rho = 0.061980 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.221046 obj = -16.368898, rho = 0.095425 nSV = 27, nBSV = 16 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) *...* optimization finished, #iter = 337 nu = 0.193471 obj = -18.456228, rho = 0.135136 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 182 nu = 0.174481 obj = -20.619082, rho = 0.214312 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 146 nu = 0.151993 obj = -22.983003, rho = 0.253945 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*...* optimization finished, #iter = 403 nu = 0.132523 obj = -25.699478, rho = 0.272380 nSV = 21, nBSV = 10 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 198 nu = 0.121720 obj = -28.617943, rho = 0.337097 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 198 nu = 0.105357 obj = -31.356698, rho = 0.383802 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 238 nu = 0.090188 obj = -34.335394, rho = 0.359252 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 160 nu = 0.079409 obj = -37.516690, rho = 0.331176 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 226 nu = 0.071358 obj = -40.033222, rho = 0.372991 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 58 nu = 0.583213 obj = -3.920029, rho = -0.356282 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.514543 obj = -4.443488, rho = -0.386206 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 42 nu = 0.466008 obj = -5.020468, rho = -0.376131 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 41 nu = 0.413902 obj = -5.638980, rho = -0.382996 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 46 nu = 0.367194 obj = -6.305423, rho = -0.335702 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 64 nu = 0.322690 obj = -7.064051, rho = -0.329390 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *..* optimization finished, #iter = 201 nu = 0.282331 obj = -7.915547, rho = -0.276132 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 87 nu = 0.245611 obj = -8.911359, rho = -0.285107 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 50 nu = 0.219482 obj = -10.047670, rho = -0.342277 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 67 nu = 0.189705 obj = -11.355496, rho = -0.357348 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 60 nu = 0.173201 obj = -12.796751, rho = -0.376863 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 63 nu = 0.153614 obj = -14.309098, rho = -0.499476 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 77 nu = 0.134770 obj = -15.963149, rho = -0.586368 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 95 nu = 0.118903 obj = -17.758190, rho = -0.648726 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 64 nu = 0.101673 obj = -19.830988, rho = -0.629550 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 48 nu = 0.091528 obj = -22.195900, rho = -0.521943 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 63 nu = 0.085129 obj = -24.388948, rho = -0.431393 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.073894 obj = -25.958282, rho = -0.424332 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 125 nu = 0.062236 obj = -27.505853, rho = -0.439919 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 163 nu = 0.055076 obj = -28.749712, rho = -0.430714 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 42 nu = 0.555283 obj = -3.705974, rho = -0.051391 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 31 nu = 0.505331 obj = -4.168198, rho = -0.078875 nSV = 52, nBSV = 49 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.443917 obj = -4.650407, rho = -0.093652 nSV = 47, nBSV = 40 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 54 nu = 0.389867 obj = -5.187488, rho = -0.069658 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 58 nu = 0.339398 obj = -5.776234, rho = -0.047108 nSV = 38, nBSV = 30 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 56 nu = 0.294398 obj = -6.456486, rho = -0.025099 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 54 nu = 0.257703 obj = -7.249873, rho = -0.045610 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 138 nu = 0.221039 obj = -8.183883, rho = -0.023898 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 86 nu = 0.197241 obj = -9.307678, rho = 0.038948 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 73 nu = 0.181347 obj = -10.503525, rho = 0.053317 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 70 nu = 0.159011 obj = -11.788118, rho = 0.085562 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.141322 obj = -13.238921, rho = 0.098031 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 283 nu = 0.124741 obj = -14.747466, rho = 0.115213 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *..* optimization finished, #iter = 213 nu = 0.108899 obj = -16.429169, rho = 0.167131 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.093871 obj = -18.422659, rho = 0.108084 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.084774 obj = -20.642610, rho = 0.041258 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.074923 obj = -22.898439, rho = -0.017797 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 62 nu = 0.067597 obj = -25.038349, rho = 0.008476 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 67 nu = 0.061524 obj = -26.756869, rho = -0.197953 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 85 nu = 0.050727 obj = -27.925358, rho = -0.171333 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 39 nu = 0.574483 obj = -3.949508, rho = 0.105516 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 96% (96/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 38 nu = 0.520000 obj = -4.489328, rho = 0.126385 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.459688 obj = -5.098404, rho = 0.107953 nSV = 50, nBSV = 41 Total nSV = 50 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 38 nu = 0.410783 obj = -5.818194, rho = 0.109835 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 57 nu = 0.363613 obj = -6.645543, rho = 0.112915 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 84 nu = 0.327970 obj = -7.601792, rho = 0.093273 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 131 nu = 0.296551 obj = -8.668375, rho = 0.083817 nSV = 35, nBSV = 26 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 65 nu = 0.272325 obj = -9.814593, rho = 0.119558 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 153 nu = 0.240957 obj = -10.994052, rho = 0.180093 nSV = 29, nBSV = 19 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 60 nu = 0.210167 obj = -12.400231, rho = 0.176245 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.187191 obj = -13.966386, rho = 0.171518 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.161515 obj = -15.777347, rho = 0.169082 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.143910 obj = -17.949131, rho = 0.147646 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.126840 obj = -20.471441, rho = 0.105761 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.113780 obj = -23.409813, rho = 0.031948 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 152 nu = 0.104763 obj = -26.600044, rho = 0.030629 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 126 nu = 0.097780 obj = -29.808850, rho = -0.029052 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.087235 obj = -32.588762, rho = -0.062969 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 159 nu = 0.075672 obj = -35.362766, rho = -0.044112 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) ..*..* optimization finished, #iter = 417 nu = 0.065611 obj = -37.963562, rho = -0.111165 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 0.653126 obj = -4.478207, rho = -0.044387 nSV = 67, nBSV = 62 Total nSV = 67 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 53 nu = 0.579223 obj = -5.118631, rho = -0.062852 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 53 nu = 0.511994 obj = -5.874636, rho = -0.063491 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 35 nu = 0.460000 obj = -6.791275, rho = -0.100296 nSV = 47, nBSV = 45 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.426064 obj = -7.817315, rho = -0.161483 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 63 nu = 0.378996 obj = -8.966992, rho = -0.229930 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 30 nu = 0.342473 obj = -10.327443, rho = -0.290123 nSV = 36, nBSV = 32 Total nSV = 36 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.313409 obj = -11.881547, rho = -0.286961 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) *..* optimization finished, #iter = 224 nu = 0.276207 obj = -13.640025, rho = -0.293994 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 98 nu = 0.249830 obj = -15.789624, rho = -0.260115 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 79 nu = 0.223605 obj = -18.252713, rho = -0.238012 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 67 nu = 0.200990 obj = -21.245918, rho = -0.187966 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 58 nu = 0.183949 obj = -24.861582, rho = -0.290283 nSV = 22, nBSV = 17 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 60 nu = 0.170248 obj = -28.949323, rho = -0.209700 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 66 nu = 0.153414 obj = -33.784222, rho = -0.228848 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 85 nu = 0.143559 obj = -39.513584, rho = -0.256356 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*...* optimization finished, #iter = 491 nu = 0.131619 obj = -45.706159, rho = -0.150646 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 251 nu = 0.120091 obj = -53.008438, rho = -0.051676 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) ...*.* optimization finished, #iter = 442 nu = 0.107319 obj = -61.485944, rho = -0.015353 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..* optimization finished, #iter = 294 nu = 0.096806 obj = -71.902085, rho = 0.016516 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 38 nu = 0.571147 obj = -3.977038, rho = -0.153897 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 51 nu = 0.521670 obj = -4.550603, rho = -0.168654 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 29 nu = 0.467470 obj = -5.192513, rho = -0.118210 nSV = 49, nBSV = 46 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 35 nu = 0.425887 obj = -5.885694, rho = -0.120195 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.381037 obj = -6.634860, rho = -0.128457 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 74 nu = 0.337314 obj = -7.458934, rho = -0.147107 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 55 nu = 0.300073 obj = -8.383086, rho = -0.207820 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.261956 obj = -9.428546, rho = -0.162551 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.233253 obj = -10.592689, rho = -0.104916 nSV = 26, nBSV = 21 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.207259 obj = -11.831760, rho = -0.099570 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 127 nu = 0.183859 obj = -13.147541, rho = -0.032287 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 156 nu = 0.159559 obj = -14.603807, rho = 0.003857 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*.* optimization finished, #iter = 314 nu = 0.140168 obj = -16.209270, rho = -0.019575 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) ...* optimization finished, #iter = 337 nu = 0.123973 obj = -17.850872, rho = -0.055877 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 188 nu = 0.108672 obj = -19.454716, rho = -0.049034 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) ...*.* optimization finished, #iter = 408 nu = 0.094071 obj = -20.904210, rho = -0.075209 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..*.* optimization finished, #iter = 391 nu = 0.077321 obj = -22.585244, rho = -0.075316 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..* optimization finished, #iter = 250 nu = 0.064532 obj = -24.695766, rho = -0.065205 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..*.* optimization finished, #iter = 380 nu = 0.054818 obj = -27.257266, rho = -0.075936 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 288 nu = 0.046866 obj = -30.395201, rho = -0.069450 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 45 nu = 0.552184 obj = -3.709118, rho = -0.107354 nSV = 57, nBSV = 54 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 39 nu = 0.493607 obj = -4.203284, rho = -0.139680 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 41 nu = 0.439999 obj = -4.742830, rho = -0.118014 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 47 nu = 0.392038 obj = -5.337479, rho = -0.042986 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 36 nu = 0.346622 obj = -6.002891, rho = -0.073005 nSV = 38, nBSV = 33 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 40 nu = 0.314002 obj = -6.693509, rho = -0.099581 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 35 nu = 0.272363 obj = -7.414655, rho = -0.089339 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 99 nu = 0.240711 obj = -8.183851, rho = -0.161709 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 86 nu = 0.210884 obj = -9.016957, rho = -0.200484 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 71 nu = 0.182373 obj = -9.896105, rho = -0.196621 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *...* optimization finished, #iter = 324 nu = 0.155468 obj = -10.815157, rho = -0.133218 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 75 nu = 0.134525 obj = -11.829624, rho = -0.155140 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 72 nu = 0.114085 obj = -12.923522, rho = -0.189762 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 212 nu = 0.098832 obj = -14.203222, rho = -0.218128 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) ..* optimization finished, #iter = 277 nu = 0.083814 obj = -15.564907, rho = -0.233359 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..*.* optimization finished, #iter = 314 nu = 0.070360 obj = -17.256663, rho = -0.223939 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..* optimization finished, #iter = 261 nu = 0.061638 obj = -19.311793, rho = -0.222625 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 287 nu = 0.055459 obj = -21.490536, rho = -0.265518 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*..* optimization finished, #iter = 317 nu = 0.048796 obj = -23.521945, rho = -0.291867 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 296 nu = 0.041113 obj = -25.879527, rho = -0.288064 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 38 nu = 0.536378 obj = -3.751987, rho = -0.099933 nSV = 57, nBSV = 51 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 43 nu = 0.491891 obj = -4.292343, rho = -0.066502 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 57 nu = 0.440062 obj = -4.882515, rho = -0.138473 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 32 nu = 0.398634 obj = -5.557411, rho = -0.204819 nSV = 41, nBSV = 38 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 53 nu = 0.355387 obj = -6.278507, rho = -0.140664 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 61 nu = 0.312387 obj = -7.108267, rho = -0.197490 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 85 nu = 0.276145 obj = -8.091362, rho = -0.194887 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 188 nu = 0.242265 obj = -9.239208, rho = -0.191286 nSV = 30, nBSV = 21 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 67 nu = 0.217315 obj = -10.617832, rho = -0.258079 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 69 nu = 0.194419 obj = -12.234537, rho = -0.344119 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 68 nu = 0.175670 obj = -14.168377, rho = -0.406544 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 69 nu = 0.158378 obj = -16.413137, rho = -0.414401 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 44 nu = 0.145466 obj = -18.989962, rho = -0.336612 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 72 nu = 0.130899 obj = -21.919034, rho = -0.267651 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.120000 obj = -25.365550, rho = -0.242514 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 59 nu = 0.109760 obj = -29.161286, rho = -0.121063 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 73 nu = 0.097498 obj = -33.569818, rho = -0.105993 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.086750 obj = -38.870433, rho = -0.141112 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 84 nu = 0.077188 obj = -45.474900, rho = -0.202647 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 83 nu = 0.072049 obj = -53.447769, rho = -0.241292 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 41 nu = 0.587444 obj = -4.021645, rho = -0.255614 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 48 nu = 0.529749 obj = -4.566596, rho = -0.229961 nSV = 56, nBSV = 49 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 39 nu = 0.467155 obj = -5.193950, rho = -0.263781 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 36 nu = 0.421334 obj = -5.898016, rho = -0.325386 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 59 nu = 0.370826 obj = -6.702874, rho = -0.317726 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 27 nu = 0.340000 obj = -7.648124, rho = -0.341635 nSV = 34, nBSV = 33 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 37 nu = 0.305670 obj = -8.599291, rho = -0.338702 nSV = 32, nBSV = 28 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 58 nu = 0.275664 obj = -9.599548, rho = -0.340629 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 67 nu = 0.242582 obj = -10.672873, rho = -0.359721 nSV = 26, nBSV = 21 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 95 nu = 0.212794 obj = -11.771990, rho = -0.371512 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *..* optimization finished, #iter = 285 nu = 0.184701 obj = -12.899822, rho = -0.382561 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *..* optimization finished, #iter = 228 nu = 0.158185 obj = -14.214006, rho = -0.350223 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.137285 obj = -15.628004, rho = -0.347070 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 175 nu = 0.117464 obj = -17.228139, rho = -0.349682 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 164 nu = 0.102321 obj = -18.919767, rho = -0.362963 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) ..* optimization finished, #iter = 246 nu = 0.086802 obj = -20.906226, rho = -0.284743 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 277 nu = 0.078343 obj = -23.014491, rho = -0.267357 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *..* optimization finished, #iter = 230 nu = 0.066586 obj = -25.010507, rho = -0.283873 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 190 nu = 0.055831 obj = -27.448893, rho = -0.278409 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 175 nu = 0.048004 obj = -30.391528, rho = -0.275107 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 49 nu = 0.532464 obj = -3.609649, rho = -0.269196 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 39 nu = 0.479428 obj = -4.085510, rho = -0.216253 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.422189 obj = -4.620908, rho = -0.200942 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 39 nu = 0.381582 obj = -5.224761, rho = -0.210420 nSV = 40, nBSV = 36 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 27 nu = 0.335384 obj = -5.888191, rho = -0.188349 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 56 nu = 0.308425 obj = -6.571394, rho = -0.134761 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 71 nu = 0.268824 obj = -7.281660, rho = -0.122542 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 64 nu = 0.237347 obj = -8.041007, rho = -0.099329 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 90 nu = 0.208649 obj = -8.777061, rho = -0.052925 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.175189 obj = -9.574287, rho = -0.045514 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 188 nu = 0.147901 obj = -10.530117, rho = -0.052386 nSV = 22, nBSV = 11 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.126894 obj = -11.684875, rho = -0.029903 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.113050 obj = -12.905095, rho = 0.060343 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 186 nu = 0.100553 obj = -14.105418, rho = 0.149172 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 94 nu = 0.086594 obj = -15.240408, rho = 0.165328 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 94 nu = 0.073698 obj = -16.456515, rho = 0.174462 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 142 nu = 0.062223 obj = -17.618871, rho = 0.202122 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 151 nu = 0.052850 obj = -18.934591, rho = 0.259698 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 182 nu = 0.047299 obj = -19.943483, rho = 0.379616 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.....* optimization finished, #iter = 627 nu = 0.039485 obj = -20.414000, rho = 0.449434 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 35 nu = 0.600000 obj = -3.924815, rho = -0.067257 nSV = 60, nBSV = 60 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 46 nu = 0.525670 obj = -4.392175, rho = -0.078561 nSV = 56, nBSV = 50 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.463274 obj = -4.932314, rho = -0.097063 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 26 nu = 0.411882 obj = -5.524045, rho = -0.087951 nSV = 42, nBSV = 39 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 80 nu = 0.361243 obj = -6.165325, rho = -0.130697 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 70 nu = 0.322783 obj = -6.854505, rho = -0.080859 nSV = 37, nBSV = 28 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 44 nu = 0.280074 obj = -7.595433, rho = -0.097798 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 80 nu = 0.247292 obj = -8.371906, rho = -0.024332 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 75 nu = 0.214703 obj = -9.172461, rho = 0.007181 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.182425 obj = -10.051844, rho = -0.020136 nSV = 22, nBSV = 11 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.154446 obj = -11.117434, rho = -0.020204 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 70 nu = 0.136037 obj = -12.345926, rho = -0.061840 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) .*.* optimization finished, #iter = 246 nu = 0.120638 obj = -13.529328, rho = -0.134845 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.103891 obj = -14.801782, rho = -0.159777 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 50 nu = 0.091961 obj = -16.058859, rho = -0.122529 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 63 nu = 0.082423 obj = -16.948136, rho = -0.108847 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) .*...*....* optimization finished, #iter = 769 nu = 0.068302 obj = -17.492940, rho = -0.080159 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .*.* optimization finished, #iter = 263 nu = 0.055709 obj = -17.868330, rho = -0.031300 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*......* optimization finished, #iter = 755 nu = 0.044461 obj = -18.230509, rho = -0.035980 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 194 nu = 0.035572 obj = -18.632720, rho = -0.094373 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 51 nu = 0.563451 obj = -3.776501, rho = -0.247464 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 66 nu = 0.499937 obj = -4.268310, rho = -0.234808 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.447401 obj = -4.814817, rho = -0.262395 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 42 nu = 0.394005 obj = -5.423134, rho = -0.250274 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 41 nu = 0.345353 obj = -6.130937, rho = -0.279984 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 95 nu = 0.303472 obj = -6.957677, rho = -0.303824 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 38 nu = 0.273476 obj = -7.920643, rho = -0.293272 nSV = 29, nBSV = 25 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 46 nu = 0.248999 obj = -8.941773, rho = -0.369141 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 127 nu = 0.215980 obj = -10.065323, rho = -0.384883 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 93 nu = 0.190653 obj = -11.405057, rho = -0.445601 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.169055 obj = -12.932221, rho = -0.484222 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 125 nu = 0.152560 obj = -14.645406, rho = -0.526638 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) *..* optimization finished, #iter = 266 nu = 0.135396 obj = -16.543775, rho = -0.547693 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.123409 obj = -18.597517, rho = -0.585015 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 158 nu = 0.109999 obj = -20.653138, rho = -0.587842 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*.* optimization finished, #iter = 274 nu = 0.095693 obj = -22.767740, rho = -0.636084 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*..* optimization finished, #iter = 431 nu = 0.080501 obj = -25.287710, rho = -0.627463 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 152 nu = 0.070337 obj = -28.431372, rho = -0.644644 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*.* optimization finished, #iter = 259 nu = 0.065821 obj = -31.504541, rho = -0.717890 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ....* optimization finished, #iter = 472 nu = 0.060534 obj = -33.577416, rho = -0.839447 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 45 nu = 0.591324 obj = -4.066811, rho = -0.097800 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 38 nu = 0.520279 obj = -4.658411, rho = -0.121020 nSV = 54, nBSV = 51 Total nSV = 54 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 29 nu = 0.476419 obj = -5.344424, rho = -0.175153 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 60 nu = 0.425088 obj = -6.119780, rho = -0.167079 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 50 nu = 0.379777 obj = -7.023562, rho = -0.130473 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 46 nu = 0.344730 obj = -8.063868, rho = -0.084482 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 41 nu = 0.315685 obj = -9.174108, rho = -0.187135 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 63 nu = 0.281747 obj = -10.419945, rho = -0.239368 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 87 nu = 0.249677 obj = -11.844465, rho = -0.199776 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.223084 obj = -13.461898, rho = -0.278131 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 80 nu = 0.208208 obj = -15.216027, rho = -0.393848 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 144 nu = 0.180850 obj = -16.986850, rho = -0.431271 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.159350 obj = -19.050626, rho = -0.435830 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 172 nu = 0.139662 obj = -21.371421, rho = -0.489052 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.121908 obj = -24.020499, rho = -0.510403 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 131 nu = 0.106742 obj = -27.167222, rho = -0.595070 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 125 nu = 0.095902 obj = -30.658858, rho = -0.631543 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 88 nu = 0.089122 obj = -34.264714, rho = -0.703731 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.081294 obj = -37.231839, rho = -0.794274 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 194 nu = 0.070588 obj = -39.475439, rho = -0.907027 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 71 nu = 0.595154 obj = -4.028497, rho = -0.234273 nSV = 62, nBSV = 55 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 34 nu = 0.535125 obj = -4.578732, rho = -0.218230 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 62 nu = 0.479761 obj = -5.178953, rho = -0.193584 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 86 nu = 0.424751 obj = -5.821005, rho = -0.177547 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 34 nu = 0.377080 obj = -6.565722, rho = -0.134804 nSV = 39, nBSV = 36 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 65 nu = 0.337156 obj = -7.358297, rho = -0.125501 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 48 nu = 0.291113 obj = -8.245378, rho = -0.114771 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 59 nu = 0.253869 obj = -9.308814, rho = -0.107106 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 42 nu = 0.230359 obj = -10.512122, rho = -0.168616 nSV = 26, nBSV = 22 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 67 nu = 0.205055 obj = -11.752474, rho = -0.182484 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 55 nu = 0.179111 obj = -13.141402, rho = -0.180092 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 165 nu = 0.156667 obj = -14.643792, rho = -0.211142 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 63 nu = 0.136788 obj = -16.399596, rho = -0.215753 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.122031 obj = -18.268218, rho = -0.210217 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 180 nu = 0.108355 obj = -20.250101, rho = -0.259174 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 158 nu = 0.093502 obj = -22.366458, rho = -0.160072 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 59 nu = 0.079505 obj = -24.844690, rho = -0.136767 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.068809 obj = -27.718775, rho = -0.101395 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 164 nu = 0.058783 obj = -31.288065, rho = -0.107390 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 160 nu = 0.050713 obj = -35.795286, rho = -0.107970 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 37 nu = 0.531118 obj = -3.583341, rho = -0.139074 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 39 nu = 0.471628 obj = -4.066439, rho = -0.134883 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 35 nu = 0.429600 obj = -4.597169, rho = -0.135367 nSV = 45, nBSV = 41 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 86 nu = 0.387087 obj = -5.141028, rho = -0.184613 nSV = 42, nBSV = 33 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 71 nu = 0.335402 obj = -5.751371, rho = -0.218323 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 65 nu = 0.290903 obj = -6.452826, rho = -0.206949 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 84 nu = 0.254282 obj = -7.258367, rho = -0.152960 nSV = 31, nBSV = 21 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 35 nu = 0.228662 obj = -8.184712, rho = -0.065495 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 88 nu = 0.201243 obj = -9.175655, rho = -0.116469 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 54 nu = 0.176076 obj = -10.308448, rho = -0.111284 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 57 nu = 0.155347 obj = -11.614895, rho = -0.105179 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 44 nu = 0.135578 obj = -13.122173, rho = -0.170921 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 68 nu = 0.121105 obj = -14.832126, rho = -0.217216 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 78 nu = 0.106136 obj = -16.821654, rho = -0.228965 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 59 nu = 0.097911 obj = -18.941454, rho = -0.254317 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.086955 obj = -21.094350, rho = -0.231753 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 145 nu = 0.079246 obj = -23.291697, rho = -0.345499 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 137 nu = 0.071797 obj = -25.104258, rho = -0.441007 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) .* optimization finished, #iter = 192 nu = 0.061364 obj = -26.226847, rho = -0.517802 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) .* optimization finished, #iter = 157 nu = 0.049772 obj = -27.314399, rho = -0.546340 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 35 nu = 0.592074 obj = -4.269190, rho = -0.189564 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 39 nu = 0.552995 obj = -4.929714, rho = -0.123134 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 52 nu = 0.494522 obj = -5.669217, rho = -0.104218 nSV = 52, nBSV = 45 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 61 nu = 0.441867 obj = -6.540909, rho = -0.172172 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 45 nu = 0.398244 obj = -7.577246, rho = -0.233069 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 48 nu = 0.362114 obj = -8.793475, rho = -0.280338 nSV = 38, nBSV = 34 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 34 nu = 0.327881 obj = -10.220141, rho = -0.345189 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 45 nu = 0.300006 obj = -11.896322, rho = -0.296651 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 71 nu = 0.283593 obj = -13.755705, rho = -0.306932 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 45 nu = 0.251232 obj = -15.816667, rho = -0.250100 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.231653 obj = -18.195380, rho = -0.279687 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.208019 obj = -20.876582, rho = -0.373735 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 196 nu = 0.186185 obj = -23.931367, rho = -0.336353 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 99 nu = 0.162999 obj = -27.661017, rho = -0.358118 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 86 nu = 0.150355 obj = -32.054077, rho = -0.549002 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 57 nu = 0.136919 obj = -37.182772, rho = -0.631272 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.126316 obj = -42.845562, rho = -0.741825 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 83 nu = 0.118438 obj = -49.023931, rho = -0.772240 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 86 nu = 0.108612 obj = -55.182197, rho = -0.791242 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.095689 obj = -61.293300, rho = -0.809010 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 49 nu = 0.556896 obj = -3.936157, rho = -0.360653 nSV = 60, nBSV = 53 Total nSV = 60 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 36 nu = 0.503712 obj = -4.528289, rho = -0.388537 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 45 nu = 0.458603 obj = -5.203294, rho = -0.351564 nSV = 49, nBSV = 42 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 47 nu = 0.425615 obj = -5.944105, rho = -0.298352 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 68 nu = 0.378227 obj = -6.743566, rho = -0.315775 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 35 nu = 0.340000 obj = -7.643052, rho = -0.315397 nSV = 36, nBSV = 32 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 59 nu = 0.300695 obj = -8.609163, rho = -0.334484 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 64 nu = 0.267123 obj = -9.736551, rho = -0.342310 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 87 nu = 0.240231 obj = -10.970674, rho = -0.316815 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 58 nu = 0.214835 obj = -12.312509, rho = -0.375613 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.186431 obj = -13.737993, rho = -0.402295 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 220 nu = 0.163384 obj = -15.375484, rho = -0.373083 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 80 nu = 0.142519 obj = -17.309403, rho = -0.364114 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 62 nu = 0.128023 obj = -19.456669, rho = -0.412494 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *..* optimization finished, #iter = 204 nu = 0.113644 obj = -21.650125, rho = -0.501449 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 170 nu = 0.099581 obj = -24.107562, rho = -0.605509 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 176 nu = 0.086955 obj = -26.695769, rho = -0.631012 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) ..* optimization finished, #iter = 240 nu = 0.075000 obj = -29.695644, rho = -0.650521 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) ..*.* optimization finished, #iter = 385 nu = 0.068100 obj = -32.834452, rho = -0.726682 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ...*.* optimization finished, #iter = 413 nu = 0.060718 obj = -35.707033, rho = -0.862235 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 38 nu = 0.570949 obj = -3.946988, rho = 0.070040 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 32 nu = 0.520083 obj = -4.512349, rho = 0.009919 nSV = 54, nBSV = 51 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 51 nu = 0.464714 obj = -5.125343, rho = 0.028508 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 51 nu = 0.415584 obj = -5.830625, rho = -0.001862 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 45 nu = 0.370831 obj = -6.627425, rho = -0.003748 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 43 nu = 0.337732 obj = -7.478331, rho = 0.037504 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 50 nu = 0.300319 obj = -8.388201, rho = 0.086626 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 63 nu = 0.260803 obj = -9.425823, rho = 0.036977 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 94 nu = 0.227224 obj = -10.643677, rho = -0.002822 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 53 nu = 0.209481 obj = -11.966022, rho = -0.111281 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 65 nu = 0.180575 obj = -13.357373, rho = -0.097989 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 52 nu = 0.161120 obj = -15.000977, rho = -0.032447 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 158 nu = 0.144399 obj = -16.569783, rho = -0.048388 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 249 nu = 0.125460 obj = -18.296896, rho = -0.001157 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 196 nu = 0.111059 obj = -20.062931, rho = 0.058235 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 80 nu = 0.093987 obj = -21.959249, rho = 0.040405 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 61 nu = 0.084175 obj = -23.865660, rho = 0.044742 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 83 nu = 0.073463 obj = -25.482217, rho = 0.126270 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.060422 obj = -26.917455, rho = 0.151860 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 159 nu = 0.050005 obj = -28.597485, rho = 0.172626 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 72 nu = 0.572633 obj = -3.924970, rho = -0.087298 nSV = 60, nBSV = 53 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 56 nu = 0.507892 obj = -4.477835, rho = -0.088473 nSV = 56, nBSV = 48 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 68 nu = 0.457976 obj = -5.124153, rho = -0.195700 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 53 nu = 0.418526 obj = -5.818338, rho = -0.244949 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 35 nu = 0.372353 obj = -6.589257, rho = -0.201015 nSV = 38, nBSV = 35 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 59 nu = 0.332242 obj = -7.437114, rho = -0.248913 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 65 nu = 0.289023 obj = -8.421565, rho = -0.266182 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 66 nu = 0.260403 obj = -9.563864, rho = -0.274113 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 47 nu = 0.229489 obj = -10.874870, rho = -0.272985 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 51 nu = 0.204271 obj = -12.401988, rho = -0.252254 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 84 nu = 0.180133 obj = -14.165585, rho = -0.202007 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 48 nu = 0.162756 obj = -16.260447, rho = -0.167407 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.148270 obj = -18.571462, rho = -0.171210 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.133722 obj = -21.118628, rho = -0.188202 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.119516 obj = -23.932730, rho = -0.241602 nSV = 19, nBSV = 8 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 168 nu = 0.106637 obj = -27.195414, rho = -0.229790 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) ..*.* optimization finished, #iter = 331 nu = 0.095569 obj = -30.594763, rho = -0.222324 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 163 nu = 0.085975 obj = -34.511907, rho = -0.152575 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *...........* optimization finished, #iter = 1145 nu = 0.075927 obj = -38.425993, rho = -0.189728 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 234 nu = 0.064367 obj = -43.155521, rho = -0.195217 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 29 nu = 0.520000 obj = -3.400089, rho = -0.255705 nSV = 54, nBSV = 51 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 39 nu = 0.462015 obj = -3.793272, rho = -0.292676 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 36 nu = 0.405203 obj = -4.231867, rho = -0.335801 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 70 nu = 0.355499 obj = -4.706911, rho = -0.327229 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 39 nu = 0.312728 obj = -5.220542, rho = -0.329091 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 48 nu = 0.272584 obj = -5.786391, rho = -0.312158 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 47 nu = 0.240874 obj = -6.393299, rho = -0.273804 nSV = 27, nBSV = 22 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 31 nu = 0.215076 obj = -6.989454, rho = -0.300359 nSV = 22, nBSV = 18 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 151 nu = 0.182595 obj = -7.521758, rho = -0.329999 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 85 nu = 0.154612 obj = -8.138125, rho = -0.358988 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 58 nu = 0.134727 obj = -8.722240, rho = -0.402851 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 71 nu = 0.111443 obj = -9.286381, rho = -0.407584 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.093837 obj = -9.939858, rho = -0.459228 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.080853 obj = -10.524693, rho = -0.518167 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 117 nu = 0.068729 obj = -10.991640, rho = -0.524019 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*..* optimization finished, #iter = 362 nu = 0.058260 obj = -11.186145, rho = -0.544066 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.046355 obj = -11.200879, rho = -0.570775 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.036377 obj = -11.200879, rho = -0.570775 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.028547 obj = -11.200879, rho = -0.570775 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.022403 obj = -11.200879, rho = -0.570775 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 42 nu = 0.540000 obj = -3.682596, rho = -0.178827 nSV = 56, nBSV = 53 Total nSV = 56 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 39 nu = 0.484986 obj = -4.189972, rho = -0.209626 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 50 nu = 0.430791 obj = -4.766406, rho = -0.205906 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 30 nu = 0.379112 obj = -5.441655, rho = -0.217219 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 63 nu = 0.341774 obj = -6.231453, rho = -0.313973 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 96% (96/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 38 nu = 0.303670 obj = -7.144985, rho = -0.299460 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 31 nu = 0.274590 obj = -8.196449, rho = -0.294153 nSV = 30, nBSV = 26 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 67 nu = 0.248439 obj = -9.370589, rho = -0.232881 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 68 nu = 0.230729 obj = -10.637167, rho = -0.080222 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.200293 obj = -12.038868, rho = -0.075801 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 86 nu = 0.182454 obj = -13.599596, rho = -0.080761 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 81 nu = 0.160433 obj = -15.315428, rho = -0.045668 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 54 nu = 0.141570 obj = -17.261587, rho = -0.035693 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 48 nu = 0.127864 obj = -19.447039, rho = -0.142166 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 68 nu = 0.115103 obj = -21.524314, rho = -0.259264 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 71 nu = 0.099255 obj = -23.879616, rho = -0.165962 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 84 nu = 0.090255 obj = -26.201954, rho = 0.138148 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 83 nu = 0.079064 obj = -28.308297, rho = 0.073196 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 86 nu = 0.071150 obj = -29.842755, rho = -0.021392 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 97 nu = 0.060253 obj = -30.135510, rho = -0.061027 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 45 nu = 0.549168 obj = -3.620512, rho = -0.019991 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 76 nu = 0.492022 obj = -4.056090, rho = -0.038883 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 56 nu = 0.433575 obj = -4.526186, rho = -0.048577 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 66 nu = 0.384938 obj = -5.033471, rho = -0.051275 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 52 nu = 0.338324 obj = -5.575199, rho = -0.044419 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 40 nu = 0.295606 obj = -6.152680, rho = -0.079480 nSV = 31, nBSV = 27 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 57 nu = 0.258101 obj = -6.728400, rho = -0.084804 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 41 nu = 0.224403 obj = -7.329263, rho = -0.112248 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.189551 obj = -7.928395, rho = -0.127981 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 177 nu = 0.165349 obj = -8.566335, rho = -0.149692 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .*.....* optimization finished, #iter = 631 nu = 0.140012 obj = -9.209293, rho = -0.146151 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) ..*.* optimization finished, #iter = 303 nu = 0.119574 obj = -9.817125, rho = -0.104183 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) .*....* optimization finished, #iter = 522 nu = 0.098857 obj = -10.421081, rho = -0.103204 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) ..* optimization finished, #iter = 268 nu = 0.081950 obj = -11.150648, rho = -0.089397 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.070039 obj = -11.898431, rho = -0.082188 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.059417 obj = -12.541368, rho = -0.090281 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 232 nu = 0.048392 obj = -13.223055, rho = -0.072628 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 164 nu = 0.040259 obj = -14.004712, rho = -0.047981 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.034373 obj = -14.708517, rho = -0.026685 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 86 nu = 0.030054 obj = -15.025274, rho = 0.042724 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 44 nu = 0.527416 obj = -3.581070, rho = -0.172299 nSV = 56, nBSV = 50 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 30 nu = 0.473410 obj = -4.071199, rho = -0.159770 nSV = 48, nBSV = 45 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 38 nu = 0.421621 obj = -4.604427, rho = -0.120618 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 33 nu = 0.384435 obj = -5.184350, rho = -0.080177 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 46 nu = 0.331845 obj = -5.817740, rho = -0.076002 nSV = 38, nBSV = 30 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 31 nu = 0.301041 obj = -6.534370, rho = -0.189738 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.260805 obj = -7.298404, rho = -0.172607 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 67 nu = 0.227948 obj = -8.180171, rho = -0.216364 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 48 nu = 0.199978 obj = -9.214711, rho = -0.089715 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 61 nu = 0.175194 obj = -10.405890, rho = -0.044106 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 71 nu = 0.157102 obj = -11.762760, rho = 0.011196 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 96 nu = 0.141942 obj = -13.185882, rho = 0.086546 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*....* optimization finished, #iter = 517 nu = 0.122313 obj = -14.723149, rho = 0.135924 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.106151 obj = -16.603625, rho = 0.177398 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 177 nu = 0.093951 obj = -18.834355, rho = 0.226876 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 223 nu = 0.084766 obj = -21.227091, rho = 0.298890 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*..* optimization finished, #iter = 450 nu = 0.073950 obj = -23.886240, rho = 0.365739 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*..* optimization finished, #iter = 302 nu = 0.065541 obj = -26.928071, rho = 0.387693 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 180 nu = 0.058862 obj = -30.345204, rho = 0.429798 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 157 nu = 0.053272 obj = -34.032338, rho = 0.522004 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 38 nu = 0.582377 obj = -3.924571, rho = -0.247854 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 41 nu = 0.520047 obj = -4.440567, rho = -0.228345 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 0.459724 obj = -5.027774, rho = -0.248803 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 68 nu = 0.407272 obj = -5.697964, rho = -0.230591 nSV = 44, nBSV = 36 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 62 nu = 0.359745 obj = -6.484897, rho = -0.213665 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 69 nu = 0.333189 obj = -7.344792, rho = -0.166277 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 60 nu = 0.297352 obj = -8.202085, rho = -0.133987 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 69 nu = 0.258740 obj = -9.175961, rho = -0.130059 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 66 nu = 0.225408 obj = -10.265339, rho = -0.221742 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 47 nu = 0.199141 obj = -11.515451, rho = -0.214386 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 71 nu = 0.178753 obj = -12.853259, rho = -0.258731 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.155733 obj = -14.250606, rho = -0.275611 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 59 nu = 0.139054 obj = -15.737741, rho = -0.242601 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 65 nu = 0.121890 obj = -17.233109, rho = -0.247293 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.108021 obj = -18.644995, rho = -0.262629 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..*..* optimization finished, #iter = 419 nu = 0.092395 obj = -19.747152, rho = -0.258193 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 165 nu = 0.075881 obj = -20.889154, rho = -0.233343 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *..* optimization finished, #iter = 237 nu = 0.063845 obj = -22.156290, rho = -0.311768 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.053998 obj = -23.222563, rho = -0.348609 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.045558 obj = -24.139862, rho = -0.357473 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 39 nu = 0.581249 obj = -3.848926, rho = -0.234846 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 34 nu = 0.521700 obj = -4.322024, rho = -0.242806 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.459712 obj = -4.809251, rho = -0.199292 nSV = 50, nBSV = 43 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 79 nu = 0.403181 obj = -5.360166, rho = -0.161214 nSV = 45, nBSV = 37 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 59 nu = 0.355576 obj = -5.962115, rho = -0.199655 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.309309 obj = -6.620204, rho = -0.222967 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.270666 obj = -7.337838, rho = -0.314552 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 78 nu = 0.239318 obj = -8.073664, rho = -0.441647 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 69 nu = 0.204938 obj = -8.885428, rho = -0.506428 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 90 nu = 0.178210 obj = -9.769687, rho = -0.461760 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 57 nu = 0.151652 obj = -10.774625, rho = -0.467789 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 73 nu = 0.136302 obj = -11.809873, rho = -0.525830 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 72 nu = 0.120882 obj = -12.710245, rho = -0.487864 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 92 nu = 0.103210 obj = -13.432456, rho = -0.468223 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 183 nu = 0.083787 obj = -14.164722, rho = -0.466769 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 88 nu = 0.070803 obj = -14.955828, rho = -0.506269 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 201 nu = 0.058480 obj = -15.738417, rho = -0.526908 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 229 nu = 0.048108 obj = -16.525395, rho = -0.512084 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 218 nu = 0.039508 obj = -17.426874, rho = -0.529614 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 138 nu = 0.034152 obj = -18.268937, rho = -0.619395 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 45 nu = 0.569785 obj = -4.002451, rho = -0.193402 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.529834 obj = -4.567003, rho = -0.137153 nSV = 54, nBSV = 51 Total nSV = 54 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 52 nu = 0.472938 obj = -5.169005, rho = -0.183878 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 40 nu = 0.418331 obj = -5.861453, rho = -0.230464 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 51 nu = 0.375234 obj = -6.621872, rho = -0.198801 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 42 nu = 0.334097 obj = -7.499508, rho = -0.266908 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 47 nu = 0.297191 obj = -8.461627, rho = -0.304546 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 61 nu = 0.261579 obj = -9.547538, rho = -0.316069 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.229884 obj = -10.804686, rho = -0.303779 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 79 nu = 0.201864 obj = -12.319189, rho = -0.277106 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 47 nu = 0.180819 obj = -14.065475, rho = -0.308792 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 47 nu = 0.163071 obj = -16.086916, rho = -0.318529 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 80 nu = 0.146552 obj = -18.293907, rho = -0.268467 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 60 nu = 0.129108 obj = -20.894368, rho = -0.319314 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.116347 obj = -23.847296, rho = -0.384112 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 161 nu = 0.106461 obj = -27.109600, rho = -0.333925 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) *..* optimization finished, #iter = 211 nu = 0.094188 obj = -30.697442, rho = -0.337866 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 94 nu = 0.083339 obj = -34.908355, rho = -0.382118 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 162 nu = 0.076170 obj = -39.518168, rho = -0.409785 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 185 nu = 0.068163 obj = -44.346895, rho = -0.509884 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 47 nu = 0.606785 obj = -4.096947, rho = -0.123152 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 51 nu = 0.549959 obj = -4.626720, rho = -0.106809 nSV = 57, nBSV = 51 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 67 nu = 0.481311 obj = -5.211105, rho = -0.102270 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 76 nu = 0.429353 obj = -5.870291, rho = -0.070762 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 67 nu = 0.386071 obj = -6.590083, rho = -0.054144 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.334983 obj = -7.359928, rho = -0.072884 nSV = 38, nBSV = 30 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 53 nu = 0.295271 obj = -8.242531, rho = -0.123906 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.261464 obj = -9.203260, rho = -0.084075 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *..* optimization finished, #iter = 282 nu = 0.234237 obj = -10.194351, rho = -0.025994 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 88 nu = 0.200569 obj = -11.253415, rho = -0.025901 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 79 nu = 0.177879 obj = -12.435416, rho = -0.169235 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 126 nu = 0.153216 obj = -13.610227, rho = -0.230446 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 89 nu = 0.131228 obj = -14.939728, rho = -0.304388 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.119119 obj = -16.251580, rho = -0.317632 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 138 nu = 0.099711 obj = -17.430690, rho = -0.298381 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 77 nu = 0.083938 obj = -18.766080, rho = -0.329187 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.073694 obj = -20.044358, rho = -0.249021 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 220 nu = 0.064684 obj = -20.786078, rho = -0.096145 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ...*.......* optimization finished, #iter = 1014 nu = 0.052721 obj = -21.031674, rho = -0.069727 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ......*.* optimization finished, #iter = 711 nu = 0.042238 obj = -21.123431, rho = -0.031921 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 35 nu = 0.544109 obj = -3.523196, rho = -0.102227 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 46 nu = 0.478026 obj = -3.917952, rho = -0.125773 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 50 nu = 0.416887 obj = -4.356264, rho = -0.090960 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 47 nu = 0.370372 obj = -4.830754, rho = -0.086670 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 39 nu = 0.319196 obj = -5.351201, rho = -0.126519 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 27 nu = 0.279590 obj = -5.938908, rho = -0.144423 nSV = 30, nBSV = 26 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 31 nu = 0.247191 obj = -6.540661, rho = -0.106973 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.210405 obj = -7.204854, rho = -0.110227 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 47 nu = 0.182816 obj = -7.960667, rho = -0.011197 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 50 nu = 0.162227 obj = -8.754266, rho = -0.011716 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.141151 obj = -9.544037, rho = 0.003113 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.121373 obj = -10.291992, rho = 0.027775 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.102381 obj = -11.064024, rho = 0.084939 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 87 nu = 0.091854 obj = -11.716402, rho = 0.028409 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 94 nu = 0.076562 obj = -12.144153, rho = -0.007817 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.063869 obj = -12.382011, rho = -0.013155 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 155 nu = 0.051533 obj = -12.452012, rho = -0.023929 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 155 nu = 0.040441 obj = -12.452012, rho = -0.023929 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 155 nu = 0.031736 obj = -12.452012, rho = -0.023929 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 155 nu = 0.024905 obj = -12.452012, rho = -0.023929 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 53 nu = 0.619438 obj = -4.271920, rho = -0.072243 nSV = 64, nBSV = 60 Total nSV = 64 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 45 nu = 0.555992 obj = -4.875783, rho = -0.039433 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 48 nu = 0.501047 obj = -5.561131, rho = -0.095663 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 48 nu = 0.451573 obj = -6.314740, rho = -0.106969 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 52 nu = 0.405032 obj = -7.170691, rho = -0.093972 nSV = 42, nBSV = 39 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 71 nu = 0.361730 obj = -8.104819, rho = -0.137766 nSV = 41, nBSV = 33 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) .* optimization finished, #iter = 171 nu = 0.317258 obj = -9.168600, rho = -0.124720 nSV = 36, nBSV = 27 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 74 nu = 0.285317 obj = -10.384699, rho = -0.078831 nSV = 34, nBSV = 25 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.256408 obj = -11.681924, rho = -0.111972 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) .*....* optimization finished, #iter = 575 nu = 0.221959 obj = -13.156850, rho = -0.125646 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 79 nu = 0.195646 obj = -14.895338, rho = -0.104365 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 125 nu = 0.174860 obj = -16.840867, rho = -0.064784 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) *....* optimization finished, #iter = 458 nu = 0.158855 obj = -18.896663, rho = -0.110128 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 217 nu = 0.138287 obj = -21.183982, rho = -0.157171 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) ...* optimization finished, #iter = 397 nu = 0.119327 obj = -23.865463, rho = -0.165773 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) ..*.* optimization finished, #iter = 385 nu = 0.103946 obj = -27.092210, rho = -0.140623 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) ....*.....* optimization finished, #iter = 917 nu = 0.092309 obj = -30.948618, rho = -0.176513 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) .......*.* optimization finished, #iter = 854 nu = 0.084948 obj = -35.304473, rho = -0.211358 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..*......* optimization finished, #iter = 866 nu = 0.077766 obj = -39.740808, rho = -0.221236 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 133 nu = 0.067106 obj = -44.581269, rho = -0.222275 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 55 nu = 0.583175 obj = -3.841418, rho = -0.036886 nSV = 62, nBSV = 55 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 52 nu = 0.520712 obj = -4.314335, rho = -0.072786 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 52 nu = 0.456900 obj = -4.832848, rho = -0.073503 nSV = 48, nBSV = 40 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 42 nu = 0.399592 obj = -5.413591, rho = -0.083581 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 77 nu = 0.351226 obj = -6.070300, rho = -0.087371 nSV = 38, nBSV = 30 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 80 nu = 0.306990 obj = -6.834126, rho = -0.017930 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 40 nu = 0.269735 obj = -7.714621, rho = 0.006398 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 68 nu = 0.243455 obj = -8.679732, rho = 0.022404 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.218597 obj = -9.672239, rho = 0.007160 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 253 nu = 0.188677 obj = -10.766080, rho = 0.004359 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 174 nu = 0.162896 obj = -11.997355, rho = 0.001432 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 91 nu = 0.145077 obj = -13.439634, rho = -0.071827 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.128904 obj = -14.848005, rho = -0.140906 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 177 nu = 0.110772 obj = -16.458726, rho = -0.171223 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.098171 obj = -18.182914, rho = -0.240509 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 194 nu = 0.086540 obj = -19.791526, rho = -0.346662 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 258 nu = 0.074400 obj = -21.429840, rho = -0.389545 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 186 nu = 0.062461 obj = -23.301145, rho = -0.375444 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 239 nu = 0.054405 obj = -25.173940, rho = -0.327196 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..* optimization finished, #iter = 260 nu = 0.046083 obj = -27.145713, rho = -0.232106 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 44 nu = 0.576851 obj = -3.870379, rho = -0.098375 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 99.4% (994/1000) (classification) * optimization finished, #iter = 47 nu = 0.517415 obj = -4.370597, rho = -0.031359 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 99.4% (994/1000) (classification) * optimization finished, #iter = 66 nu = 0.449769 obj = -4.928498, rho = -0.010760 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 99.4% (994/1000) (classification) * optimization finished, #iter = 47 nu = 0.400090 obj = -5.595141, rho = 0.043731 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 99.4% (994/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.355529 obj = -6.332144, rho = 0.097158 nSV = 40, nBSV = 31 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 99.3% (993/1000) (classification) * optimization finished, #iter = 53 nu = 0.312077 obj = -7.212962, rho = 0.125991 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 64 nu = 0.283000 obj = -8.229481, rho = 0.108501 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) .* optimization finished, #iter = 117 nu = 0.251938 obj = -9.346224, rho = 0.052642 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 88 nu = 0.224780 obj = -10.656812, rho = 0.017301 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.200139 obj = -12.143081, rho = 0.019408 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.176596 obj = -13.885286, rho = -0.044674 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 79 nu = 0.159370 obj = -15.918745, rho = -0.170336 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 42 nu = 0.144074 obj = -18.205029, rho = -0.270035 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.128166 obj = -20.840109, rho = -0.225461 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.120512 obj = -23.687091, rho = -0.157286 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 92 nu = 0.109211 obj = -26.417611, rho = -0.069288 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.095811 obj = -29.249369, rho = -0.080033 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.083990 obj = -32.303522, rho = -0.095380 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 268 nu = 0.074287 obj = -35.465479, rho = -0.104876 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.064019 obj = -38.726184, rho = -0.098319 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 58 nu = 0.547192 obj = -3.576828, rho = -0.072727 nSV = 60, nBSV = 51 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 52 nu = 0.478455 obj = -4.014065, rho = -0.041225 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 136 nu = 0.421563 obj = -4.508389, rho = -0.056107 nSV = 47, nBSV = 38 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 50 nu = 0.374597 obj = -5.069139, rho = -0.052885 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 41 nu = 0.328558 obj = -5.684528, rho = -0.023993 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 38 nu = 0.298953 obj = -6.344606, rho = 0.053717 nSV = 30, nBSV = 28 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 62 nu = 0.261895 obj = -6.988036, rho = 0.018081 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.226531 obj = -7.690079, rho = 0.101734 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 80 nu = 0.191528 obj = -8.493611, rho = 0.166285 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.165255 obj = -9.449857, rho = 0.193552 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 70 nu = 0.142620 obj = -10.592532, rho = 0.209495 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.131125 obj = -11.782783, rho = 0.348660 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 184 nu = 0.112740 obj = -12.945740, rho = 0.426090 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.......* optimization finished, #iter = 755 nu = 0.099412 obj = -14.224483, rho = 0.516570 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..* optimization finished, #iter = 298 nu = 0.086421 obj = -15.511766, rho = 0.540698 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*..* optimization finished, #iter = 375 nu = 0.073545 obj = -16.843231, rho = 0.546167 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..*.....* optimization finished, #iter = 762 nu = 0.062387 obj = -18.315257, rho = 0.586297 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) ...*...* optimization finished, #iter = 604 nu = 0.051883 obj = -20.091357, rho = 0.586865 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 90 nu = 0.044104 obj = -22.305255, rho = 0.573966 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 70 nu = 0.038841 obj = -24.849985, rho = 0.503814 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 42 nu = 0.616621 obj = -4.213432, rho = -0.103217 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 41 nu = 0.547697 obj = -4.804159, rho = -0.076269 nSV = 56, nBSV = 53 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 35 nu = 0.493480 obj = -5.472601, rho = -0.160214 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 41 nu = 0.440604 obj = -6.239212, rho = -0.155090 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.387532 obj = -7.128356, rho = -0.171678 nSV = 42, nBSV = 34 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 49 nu = 0.345516 obj = -8.197369, rho = -0.165002 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 39 nu = 0.314904 obj = -9.445477, rho = -0.267663 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 74 nu = 0.285898 obj = -10.816202, rho = -0.287291 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 33 nu = 0.262276 obj = -12.374365, rho = -0.206244 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 48 nu = 0.237012 obj = -13.965571, rho = -0.146534 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 75 nu = 0.215962 obj = -15.670506, rho = -0.039524 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 88 nu = 0.196679 obj = -17.345290, rho = -0.096855 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 140 nu = 0.170331 obj = -18.989285, rho = -0.130889 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.144244 obj = -20.762126, rho = -0.155977 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 95 nu = 0.127260 obj = -22.721208, rho = -0.178192 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 126 nu = 0.110653 obj = -24.426728, rho = -0.275101 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 186 nu = 0.091493 obj = -26.229667, rho = -0.314234 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 136 nu = 0.075606 obj = -28.487743, rho = -0.318273 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.065455 obj = -31.163785, rho = -0.357321 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 212 nu = 0.056761 obj = -33.618980, rho = -0.385182 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.584388 obj = -4.013829, rho = -0.387443 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 35 nu = 0.517773 obj = -4.597499, rho = -0.410081 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 37 nu = 0.479069 obj = -5.264871, rho = -0.399038 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 45 nu = 0.424880 obj = -5.979166, rho = -0.362647 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 83 nu = 0.384716 obj = -6.740369, rho = -0.288424 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 94 nu = 0.340389 obj = -7.578892, rho = -0.245948 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 54 nu = 0.295363 obj = -8.565645, rho = -0.242216 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 45 nu = 0.272108 obj = -9.688659, rho = -0.225287 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 80 nu = 0.240917 obj = -10.824355, rho = -0.157010 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 270 nu = 0.211384 obj = -12.025263, rho = -0.101107 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*..* optimization finished, #iter = 337 nu = 0.179837 obj = -13.456432, rho = -0.096963 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.160371 obj = -15.179190, rho = -0.112098 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 237 nu = 0.139949 obj = -17.052146, rho = -0.138418 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*..* optimization finished, #iter = 309 nu = 0.121130 obj = -19.318936, rho = -0.154953 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 221 nu = 0.106578 obj = -22.076345, rho = -0.181430 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*..* optimization finished, #iter = 349 nu = 0.099429 obj = -25.112739, rho = -0.261412 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) .....*...* optimization finished, #iter = 899 nu = 0.089015 obj = -28.240186, rho = -0.259147 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..* optimization finished, #iter = 296 nu = 0.077611 obj = -31.864776, rho = -0.264407 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) ....* optimization finished, #iter = 456 nu = 0.066768 obj = -36.158061, rho = -0.258207 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) ...*.* optimization finished, #iter = 431 nu = 0.059365 obj = -41.534089, rho = -0.225739 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 74 nu = 0.493339 obj = -3.286040, rho = -0.153593 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 49 nu = 0.440067 obj = -3.705164, rho = -0.189974 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.389303 obj = -4.153146, rho = -0.189230 nSV = 45, nBSV = 36 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 40 nu = 0.343628 obj = -4.668758, rho = -0.136497 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 45 nu = 0.298900 obj = -5.255149, rho = -0.210724 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 94 nu = 0.266901 obj = -5.917952, rho = -0.205790 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 91 nu = 0.233554 obj = -6.670678, rho = -0.221702 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 71 nu = 0.209780 obj = -7.518708, rho = -0.295748 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 97 nu = 0.188071 obj = -8.404650, rho = -0.353877 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.162661 obj = -9.367830, rho = -0.420863 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 94 nu = 0.141635 obj = -10.518581, rho = -0.440004 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 91 nu = 0.124515 obj = -11.787114, rho = -0.488502 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.110565 obj = -13.235649, rho = -0.559535 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 71 nu = 0.095865 obj = -14.895244, rho = -0.558827 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.083401 obj = -16.829419, rho = -0.557365 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 51 nu = 0.072811 obj = -19.223504, rho = -0.594207 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.068356 obj = -21.855828, rho = -0.800863 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 115 nu = 0.062913 obj = -24.447524, rho = -1.056611 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) *.* optimization finished, #iter = 160 nu = 0.056583 obj = -26.587089, rho = -1.304808 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.1% (961/1000) (classification) *..* optimization finished, #iter = 219 nu = 0.047678 obj = -28.917317, rho = -1.328852 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 35 nu = 0.536839 obj = -3.507063, rho = -0.288516 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 61 nu = 0.474521 obj = -3.927099, rho = -0.281416 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 57 nu = 0.419275 obj = -4.381654, rho = -0.280790 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.367193 obj = -4.874302, rho = -0.255925 nSV = 41, nBSV = 32 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 37 nu = 0.318797 obj = -5.436575, rho = -0.231579 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 32 nu = 0.282417 obj = -6.057449, rho = -0.240120 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 38 nu = 0.250085 obj = -6.718989, rho = -0.169318 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 34 nu = 0.218565 obj = -7.386021, rho = -0.190000 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 36 nu = 0.188232 obj = -8.109897, rho = -0.195553 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 70 nu = 0.165971 obj = -8.879115, rho = -0.290020 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 79 nu = 0.140872 obj = -9.662928, rho = -0.276006 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 64 nu = 0.121509 obj = -10.551791, rho = -0.244842 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 60 nu = 0.104793 obj = -11.399707, rho = -0.254144 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 68 nu = 0.087588 obj = -12.362294, rho = -0.249814 nSV = 11, nBSV = 5 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.075592 obj = -13.367890, rho = -0.259868 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 89 nu = 0.066687 obj = -14.361939, rho = -0.328799 nSV = 10, nBSV = 4 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 60 nu = 0.058996 obj = -14.885095, rho = -0.412669 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 52 nu = 0.048476 obj = -15.115036, rho = -0.332189 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 59 nu = 0.038543 obj = -15.122849, rho = -0.302211 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 59 nu = 0.030247 obj = -15.122849, rho = -0.302211 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 42 nu = 0.553437 obj = -3.838410, rho = 0.046429 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 38 nu = 0.500000 obj = -4.383541, rho = -0.013540 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 35 nu = 0.451989 obj = -4.989859, rho = -0.053569 nSV = 48, nBSV = 44 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 45 nu = 0.416995 obj = -5.630193, rho = -0.161347 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 86 nu = 0.367086 obj = -6.287696, rho = -0.159147 nSV = 42, nBSV = 33 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 90 nu = 0.319312 obj = -7.051647, rho = -0.174012 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 89 nu = 0.282056 obj = -7.924992, rho = -0.165086 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 68 nu = 0.246645 obj = -8.929561, rho = -0.158266 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 55 nu = 0.214819 obj = -10.107465, rho = -0.132923 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 57 nu = 0.191227 obj = -11.487760, rho = -0.085401 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 160 nu = 0.167690 obj = -13.066964, rho = -0.072201 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) *..* optimization finished, #iter = 235 nu = 0.146696 obj = -15.015263, rho = -0.074820 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.130545 obj = -17.423244, rho = -0.086383 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 72 nu = 0.119230 obj = -20.299672, rho = -0.149140 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 76 nu = 0.111056 obj = -23.574155, rho = -0.245348 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 61 nu = 0.104309 obj = -27.038558, rho = -0.369691 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 85 nu = 0.096280 obj = -30.543417, rho = -0.470478 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.086714 obj = -34.040181, rho = -0.579546 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 78 nu = 0.073392 obj = -38.049604, rho = -0.553055 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.063184 obj = -43.054373, rho = -0.517593 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 49 nu = 0.574159 obj = -3.718390, rho = -0.218943 nSV = 58, nBSV = 54 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 69 nu = 0.500490 obj = -4.156393, rho = -0.194109 nSV = 54, nBSV = 46 Total nSV = 54 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 41 nu = 0.431274 obj = -4.669610, rho = -0.207675 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 31 nu = 0.387108 obj = -5.253511, rho = -0.184246 nSV = 40, nBSV = 36 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 39 nu = 0.341475 obj = -5.886273, rho = -0.132267 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 53 nu = 0.299605 obj = -6.593856, rho = -0.102509 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 85 nu = 0.266055 obj = -7.382276, rho = -0.129476 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *..* optimization finished, #iter = 205 nu = 0.234145 obj = -8.240922, rho = -0.078648 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.203501 obj = -9.197854, rho = -0.118868 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 67 nu = 0.177528 obj = -10.301953, rho = -0.170757 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.154747 obj = -11.580728, rho = -0.214826 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 82 nu = 0.138809 obj = -13.028321, rho = -0.239913 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 77 nu = 0.122204 obj = -14.522672, rho = -0.339200 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 75 nu = 0.106727 obj = -16.212681, rho = -0.380946 nSV = 19, nBSV = 8 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 57 nu = 0.096606 obj = -18.109846, rho = -0.356114 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 73 nu = 0.086332 obj = -19.897284, rho = -0.326914 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 70 nu = 0.074528 obj = -21.610792, rho = -0.380648 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 41 nu = 0.067607 obj = -23.140114, rho = -0.672255 nSV = 9, nBSV = 3 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 51 nu = 0.058452 obj = -24.003505, rho = -0.693757 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 58 nu = 0.048323 obj = -24.161567, rho = -0.670785 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 37 nu = 0.558849 obj = -3.881684, rho = -0.114940 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 42 nu = 0.500660 obj = -4.445737, rho = -0.096104 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 38 nu = 0.452500 obj = -5.107627, rho = -0.095717 nSV = 47, nBSV = 43 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 37 nu = 0.408878 obj = -5.851891, rho = -0.105177 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 53 nu = 0.367567 obj = -6.689393, rho = -0.165814 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 56 nu = 0.329452 obj = -7.639722, rho = -0.182700 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 64 nu = 0.295297 obj = -8.732839, rho = -0.200977 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 62 nu = 0.259849 obj = -10.018876, rho = -0.204200 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 42 nu = 0.240564 obj = -11.500616, rho = -0.224355 nSV = 26, nBSV = 22 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 42 nu = 0.215275 obj = -13.134353, rho = -0.132938 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 60 nu = 0.201433 obj = -14.882674, rho = -0.044451 nSV = 22, nBSV = 17 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.177782 obj = -16.589535, rho = -0.026466 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 77 nu = 0.158385 obj = -18.463738, rho = 0.009413 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 57 nu = 0.138909 obj = -20.416137, rho = -0.041889 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 74 nu = 0.120252 obj = -22.604408, rho = -0.047005 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 171 nu = 0.106328 obj = -24.800146, rho = -0.122903 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 195 nu = 0.093921 obj = -27.027998, rho = -0.239447 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 143 nu = 0.081103 obj = -29.185864, rho = -0.116454 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 138 nu = 0.071169 obj = -31.072742, rho = -0.172376 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 154 nu = 0.062617 obj = -31.909131, rho = -0.264529 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.564577 obj = -3.837136, rho = 0.092871 nSV = 59, nBSV = 56 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.515275 obj = -4.333328, rho = 0.081764 nSV = 53, nBSV = 49 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 59 nu = 0.447844 obj = -4.888569, rho = 0.084034 nSV = 49, nBSV = 42 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 61 nu = 0.397581 obj = -5.550134, rho = 0.118846 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 72 nu = 0.355240 obj = -6.299976, rho = 0.071482 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 59 nu = 0.323263 obj = -7.098358, rho = -0.023310 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.281810 obj = -7.970466, rho = -0.049868 nSV = 32, nBSV = 23 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.242358 obj = -9.018225, rho = -0.064402 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 87 nu = 0.218903 obj = -10.215376, rho = -0.154158 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 88 nu = 0.194828 obj = -11.578401, rho = -0.206365 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 75 nu = 0.170498 obj = -13.134287, rho = -0.176189 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 35 nu = 0.154700 obj = -14.955990, rho = -0.111212 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 73 nu = 0.139602 obj = -16.864448, rho = -0.084742 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 66 nu = 0.124778 obj = -18.915620, rho = -0.063271 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 95 nu = 0.110760 obj = -21.168437, rho = -0.116338 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 88 nu = 0.097602 obj = -23.486745, rho = -0.114939 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 75 nu = 0.082627 obj = -26.190338, rho = -0.084231 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 67 nu = 0.072241 obj = -29.432271, rho = -0.088239 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 91 nu = 0.063285 obj = -33.193178, rho = -0.143986 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) .*.* optimization finished, #iter = 258 nu = 0.055513 obj = -37.604974, rho = -0.227930 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 46 nu = 0.559270 obj = -3.786448, rho = -0.184682 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 44 nu = 0.499861 obj = -4.292855, rho = -0.217829 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 38 nu = 0.438179 obj = -4.876296, rho = -0.191381 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 36 nu = 0.395481 obj = -5.556971, rho = -0.127461 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 65 nu = 0.345991 obj = -6.338419, rho = -0.143403 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 37 nu = 0.314366 obj = -7.250170, rho = -0.213912 nSV = 33, nBSV = 30 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 63 nu = 0.284205 obj = -8.214569, rho = -0.123698 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 37 nu = 0.250179 obj = -9.352956, rho = -0.144313 nSV = 27, nBSV = 24 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 41 nu = 0.238433 obj = -10.558632, rho = -0.286109 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 65 nu = 0.204613 obj = -11.736571, rho = -0.314088 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.175955 obj = -13.180141, rho = -0.350530 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *..* optimization finished, #iter = 297 nu = 0.157748 obj = -14.838820, rho = -0.307748 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *..* optimization finished, #iter = 228 nu = 0.136669 obj = -16.705202, rho = -0.284946 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 162 nu = 0.121740 obj = -18.869951, rho = -0.275060 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 155 nu = 0.113127 obj = -21.069304, rho = -0.305486 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 153 nu = 0.103910 obj = -22.887057, rho = -0.332459 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..*.* optimization finished, #iter = 308 nu = 0.088456 obj = -24.444578, rho = -0.368461 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 290 nu = 0.073243 obj = -26.043212, rho = -0.281717 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 153 nu = 0.061144 obj = -27.914501, rho = -0.340588 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.051536 obj = -29.727918, rho = -0.431735 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 34 nu = 0.567225 obj = -4.017965, rho = -0.130644 nSV = 58, nBSV = 54 Total nSV = 58 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 42 nu = 0.509653 obj = -4.643387, rho = -0.108882 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 60 nu = 0.468132 obj = -5.364707, rho = -0.168860 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 78 nu = 0.421336 obj = -6.186247, rho = -0.169218 nSV = 47, nBSV = 38 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.379333 obj = -7.143144, rho = -0.181936 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.347023 obj = -8.257901, rho = -0.104678 nSV = 39, nBSV = 30 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 36 nu = 0.311670 obj = -9.548740, rho = -0.113527 nSV = 34, nBSV = 30 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 53 nu = 0.289124 obj = -10.967139, rho = -0.035741 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 65 nu = 0.255244 obj = -12.613115, rho = -0.051571 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 84 nu = 0.233316 obj = -14.539755, rho = -0.036783 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 37 nu = 0.217386 obj = -16.630726, rho = -0.028018 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 90 nu = 0.192885 obj = -18.864547, rho = -0.005934 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.173460 obj = -21.411724, rho = -0.085057 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.160459 obj = -24.168440, rho = -0.086246 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 250 nu = 0.139663 obj = -26.883381, rho = -0.140711 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.119060 obj = -30.214218, rho = -0.172990 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 153 nu = 0.105334 obj = -34.098301, rho = -0.267811 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.097034 obj = -38.407804, rho = -0.483916 nSV = 13, nBSV = 8 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.092508 obj = -41.848059, rho = -0.722014 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.081691 obj = -43.967960, rho = -0.880281 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 36 nu = 0.520000 obj = -3.494806, rho = -0.109883 nSV = 53, nBSV = 49 Total nSV = 53 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 52 nu = 0.468960 obj = -3.930444, rho = -0.064721 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 51 nu = 0.416754 obj = -4.402498, rho = -0.063656 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 29 nu = 0.364936 obj = -4.926103, rho = -0.038854 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 54 nu = 0.318458 obj = -5.520828, rho = -0.052381 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 41 nu = 0.284364 obj = -6.183104, rho = -0.063876 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 83 nu = 0.249320 obj = -6.910807, rho = -0.096783 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.214144 obj = -7.755500, rho = -0.121138 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 80 nu = 0.190083 obj = -8.735765, rho = -0.121529 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 69 nu = 0.168677 obj = -9.828487, rho = -0.154321 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 71 nu = 0.151492 obj = -10.976189, rho = -0.134271 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 65 nu = 0.129104 obj = -12.290389, rho = -0.148099 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 56 nu = 0.114465 obj = -13.862552, rho = -0.210063 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 98 nu = 0.100813 obj = -15.548588, rho = -0.213817 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*..* optimization finished, #iter = 334 nu = 0.088681 obj = -17.543264, rho = -0.269401 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 132 nu = 0.079613 obj = -19.786795, rho = -0.289328 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 130 nu = 0.069818 obj = -22.192814, rho = -0.318724 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.064449 obj = -24.710097, rho = -0.296244 nSV = 10, nBSV = 4 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 214 nu = 0.058440 obj = -26.734545, rho = -0.210896 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.050273 obj = -28.545999, rho = -0.228773 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 45 nu = 0.516675 obj = -3.341616, rho = -0.167204 nSV = 54, nBSV = 47 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 56 nu = 0.449860 obj = -3.742257, rho = -0.133674 nSV = 50, nBSV = 43 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 60 nu = 0.395885 obj = -4.182953, rho = -0.118760 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 83 nu = 0.341849 obj = -4.698365, rho = -0.115973 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 42 nu = 0.301230 obj = -5.302991, rho = -0.099944 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 48 nu = 0.263056 obj = -6.009335, rho = -0.090165 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 37 nu = 0.236333 obj = -6.798937, rho = -0.101340 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.206782 obj = -7.703356, rho = -0.070707 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 38 nu = 0.184509 obj = -8.771370, rho = -0.023798 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 57 nu = 0.163750 obj = -10.008448, rho = -0.043970 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.146475 obj = -11.436403, rho = -0.123988 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.131689 obj = -13.092881, rho = -0.198664 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 52 nu = 0.121441 obj = -14.927267, rho = -0.285646 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 94 nu = 0.105559 obj = -16.919858, rho = -0.275483 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 70 nu = 0.093394 obj = -19.354590, rho = -0.220568 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.084266 obj = -22.211801, rho = -0.150118 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 71 nu = 0.074240 obj = -25.572241, rho = -0.182168 nSV = 11, nBSV = 5 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 31 nu = 0.068847 obj = -29.429670, rho = -0.074895 nSV = 10, nBSV = 4 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.062519 obj = -33.544837, rho = 0.072159 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.055921 obj = -38.226428, rho = 0.215000 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.577883 obj = -3.944414, rho = -0.169874 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 48 nu = 0.530151 obj = -4.467934, rho = -0.173803 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 59 nu = 0.465593 obj = -5.031358, rho = -0.158149 nSV = 51, nBSV = 43 Total nSV = 51 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 41 nu = 0.406856 obj = -5.695787, rho = -0.154053 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.363619 obj = -6.445549, rho = -0.167560 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 50 nu = 0.319499 obj = -7.320680, rho = -0.171183 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 41 nu = 0.282053 obj = -8.363344, rho = -0.171929 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 96% (96/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 55 nu = 0.259136 obj = -9.547947, rho = -0.147455 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 96% (96/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.227152 obj = -10.867848, rho = -0.148964 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 96% (96/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.201292 obj = -12.426684, rho = -0.168752 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 96% (96/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 188 nu = 0.176363 obj = -14.332706, rho = -0.173400 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 96% (96/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 97 nu = 0.160431 obj = -16.617339, rho = -0.281662 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.144078 obj = -19.326439, rho = -0.303716 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 87 nu = 0.136118 obj = -22.452868, rho = -0.501185 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 79 nu = 0.122614 obj = -25.796285, rho = -0.557707 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 53 nu = 0.115685 obj = -29.549404, rho = -0.728786 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) *..* optimization finished, #iter = 215 nu = 0.104932 obj = -33.253356, rho = -0.799889 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 155 nu = 0.095532 obj = -37.171412, rho = -0.845336 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 168 nu = 0.085826 obj = -40.881500, rho = -0.821987 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .* optimization finished, #iter = 170 nu = 0.076830 obj = -44.053367, rho = -0.818357 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 35 nu = 0.488801 obj = -3.197213, rho = -0.050272 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.441189 obj = -3.558510, rho = -0.071486 nSV = 47, nBSV = 40 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 59 nu = 0.380269 obj = -3.943214, rho = -0.071597 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 34 nu = 0.334643 obj = -4.381998, rho = -0.047160 nSV = 34, nBSV = 31 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 53 nu = 0.287959 obj = -4.849252, rho = -0.057302 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 26 nu = 0.259992 obj = -5.369541, rho = -0.150894 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 27 nu = 0.228097 obj = -5.858477, rho = -0.084836 nSV = 25, nBSV = 21 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 31 nu = 0.195446 obj = -6.338675, rho = -0.102829 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.172093 obj = -6.766419, rho = -0.188799 nSV = 19, nBSV = 14 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 135 nu = 0.143645 obj = -7.143350, rho = -0.228173 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 162 nu = 0.123788 obj = -7.490397, rho = -0.219105 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 185 nu = 0.101085 obj = -7.707571, rho = -0.190322 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 89 nu = 0.082587 obj = -7.911057, rho = -0.213113 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 173 nu = 0.067526 obj = -8.049381, rho = -0.199823 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 224 nu = 0.054262 obj = -8.074648, rho = -0.237929 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 190 nu = 0.042579 obj = -8.074650, rho = -0.237469 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 190 nu = 0.033415 obj = -8.074650, rho = -0.237469 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 190 nu = 0.026222 obj = -8.074650, rho = -0.237469 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 190 nu = 0.020578 obj = -8.074650, rho = -0.237469 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 190 nu = 0.016149 obj = -8.074650, rho = -0.237469 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.552156 obj = -3.622625, rho = -0.166264 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 0.483129 obj = -4.068995, rho = -0.127276 nSV = 53, nBSV = 46 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 70 nu = 0.432196 obj = -4.573273, rho = -0.160490 nSV = 47, nBSV = 39 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 41 nu = 0.380795 obj = -5.119565, rho = -0.213969 nSV = 41, nBSV = 37 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 54 nu = 0.333370 obj = -5.710940, rho = -0.230551 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.285270 obj = -6.420450, rho = -0.233964 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 41 nu = 0.256158 obj = -7.254091, rho = -0.241515 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.228306 obj = -8.135265, rho = -0.195153 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 87 nu = 0.205393 obj = -9.046076, rho = -0.152525 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.176410 obj = -10.036360, rho = -0.153918 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.151068 obj = -11.218856, rho = -0.152016 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 168 nu = 0.131978 obj = -12.629096, rho = -0.215468 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 140 nu = 0.114639 obj = -14.296978, rho = -0.211906 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 139 nu = 0.102600 obj = -16.243162, rho = -0.111894 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 81 nu = 0.092095 obj = -18.481512, rho = -0.045607 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 119 nu = 0.083864 obj = -20.856799, rho = -0.006765 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 87 nu = 0.078545 obj = -23.106010, rho = 0.104207 nSV = 10, nBSV = 4 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 89 nu = 0.067361 obj = -25.111970, rho = 0.141447 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 87 nu = 0.058347 obj = -27.247699, rho = 0.179555 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *..* optimization finished, #iter = 276 nu = 0.049663 obj = -29.344983, rho = 0.242656 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 35 nu = 0.509503 obj = -3.392586, rho = -0.167057 nSV = 53, nBSV = 49 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 31 nu = 0.454515 obj = -3.813285, rho = -0.169409 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 47 nu = 0.410110 obj = -4.247621, rho = -0.092921 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 57 nu = 0.363890 obj = -4.706117, rho = -0.073372 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 85 nu = 0.313495 obj = -5.197966, rho = -0.082502 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 72 nu = 0.274392 obj = -5.737456, rho = -0.032252 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.237845 obj = -6.290825, rho = 0.001313 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.202925 obj = -6.914156, rho = 0.022006 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 97 nu = 0.171409 obj = -7.661810, rho = 0.014296 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 78 nu = 0.152829 obj = -8.503827, rho = -0.087449 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 154 nu = 0.133355 obj = -9.330724, rho = -0.119518 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 61 nu = 0.115301 obj = -10.278589, rho = -0.151616 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) *.* optimization finished, #iter = 148 nu = 0.101541 obj = -11.143601, rho = -0.270799 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 168 nu = 0.088169 obj = -12.037177, rho = -0.275204 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 95 nu = 0.074057 obj = -12.917619, rho = -0.243542 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 257 nu = 0.061058 obj = -13.956013, rho = -0.233244 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 175 nu = 0.051788 obj = -15.204332, rho = -0.217709 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 88 nu = 0.045160 obj = -16.502570, rho = -0.214794 nSV = 8, nBSV = 2 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 80 nu = 0.040052 obj = -17.629858, rho = -0.259756 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 86 nu = 0.034670 obj = -18.378682, rho = -0.146765 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 40 nu = 0.606671 obj = -4.197881, rho = -0.034620 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 50 nu = 0.544480 obj = -4.804489, rho = 0.003159 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 66 nu = 0.493589 obj = -5.475377, rho = 0.046623 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 41 nu = 0.441717 obj = -6.250631, rho = 0.029289 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 78 nu = 0.394289 obj = -7.111744, rho = 0.009814 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 46 nu = 0.356825 obj = -8.109625, rho = 0.006501 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 65 nu = 0.329018 obj = -9.145105, rho = 0.093306 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.281356 obj = -10.282260, rho = 0.096367 nSV = 34, nBSV = 24 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 73 nu = 0.250897 obj = -11.637210, rho = 0.143832 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 42 nu = 0.222859 obj = -13.181337, rho = 0.083601 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.195873 obj = -14.877695, rho = 0.100997 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 80 nu = 0.174975 obj = -16.799887, rho = 0.078386 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 136 nu = 0.157187 obj = -18.910599, rho = 0.131782 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.135737 obj = -21.267606, rho = 0.120779 nSV = 20, nBSV = 9 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.118877 obj = -24.216059, rho = 0.110467 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 163 nu = 0.108632 obj = -27.440812, rho = 0.054993 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*..* optimization finished, #iter = 332 nu = 0.098739 obj = -30.745166, rho = 0.010966 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 96 nu = 0.089859 obj = -34.064317, rho = 0.277603 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 171 nu = 0.077745 obj = -37.111237, rho = 0.483738 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 199 nu = 0.067307 obj = -40.139804, rho = 0.569641 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 61 nu = 0.502006 obj = -3.573225, rho = -0.389775 nSV = 54, nBSV = 47 Total nSV = 54 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 61 nu = 0.456403 obj = -4.134049, rho = -0.383318 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 38 nu = 0.415136 obj = -4.776986, rho = -0.370375 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 29 nu = 0.380000 obj = -5.511721, rho = -0.371055 nSV = 39, nBSV = 36 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 45 nu = 0.349866 obj = -6.294852, rho = -0.290632 nSV = 38, nBSV = 30 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 59 nu = 0.310618 obj = -7.190486, rho = -0.295518 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 37 nu = 0.280890 obj = -8.193210, rho = -0.339336 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 48 nu = 0.248054 obj = -9.329334, rho = -0.456335 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 52 nu = 0.224095 obj = -10.620702, rho = -0.611657 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 38 nu = 0.196205 obj = -12.149492, rho = -0.587890 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 29 nu = 0.177002 obj = -13.911772, rho = -0.711201 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 26 nu = 0.160000 obj = -15.987103, rho = -0.664359 nSV = 18, nBSV = 14 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 62 nu = 0.150092 obj = -18.082082, rho = -0.510920 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 92 nu = 0.137753 obj = -20.154747, rho = -0.504761 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.116520 obj = -22.322689, rho = -0.524828 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 189 nu = 0.099243 obj = -25.023320, rho = -0.532566 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 207 nu = 0.087905 obj = -28.253623, rho = -0.454676 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.075972 obj = -32.005171, rho = -0.471609 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.069830 obj = -36.344909, rho = -0.539004 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 156 nu = 0.067602 obj = -40.281269, rho = -0.735984 nSV = 10, nBSV = 4 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 36 nu = 0.600534 obj = -4.145695, rho = -0.049723 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 96% (96/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 47 nu = 0.537909 obj = -4.740401, rho = -0.001402 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 96% (96/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 41 nu = 0.491504 obj = -5.394628, rho = -0.085709 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 95% (95/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 48 nu = 0.439521 obj = -6.116285, rho = -0.036599 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 95% (95/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 74 nu = 0.389447 obj = -6.934099, rho = 0.011096 nSV = 42, nBSV = 34 Total nSV = 42 Accuracy = 96% (96/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.338128 obj = -7.898762, rho = 0.031103 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 75 nu = 0.299859 obj = -9.080935, rho = -0.026181 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 96% (96/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 86 nu = 0.272391 obj = -10.458475, rho = 0.050679 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 65 nu = 0.244455 obj = -12.044407, rho = 0.031955 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 74 nu = 0.215593 obj = -13.962140, rho = 0.044981 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 96% (96/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 64 nu = 0.196739 obj = -16.299417, rho = 0.051148 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 60 nu = 0.182324 obj = -18.990038, rho = 0.059801 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 96% (96/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 80 nu = 0.164796 obj = -22.097622, rho = -0.032539 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 85 nu = 0.147771 obj = -25.829092, rho = -0.106698 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.133034 obj = -30.476089, rho = -0.139989 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.125993 obj = -36.091163, rho = -0.207138 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 78 nu = 0.121144 obj = -42.168541, rho = -0.325038 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) ....*...* optimization finished, #iter = 762 nu = 0.108028 obj = -48.995877, rho = -0.349401 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) ....*..* optimization finished, #iter = 687 nu = 0.096047 obj = -57.553433, rho = -0.341647 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 141 nu = 0.089248 obj = -68.260055, rho = -0.341488 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 40 nu = 0.593825 obj = -4.167065, rho = -0.064975 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 97% (97/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 57 nu = 0.540786 obj = -4.776514, rho = -0.031725 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 97% (97/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 44 nu = 0.483867 obj = -5.479059, rho = -0.030400 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 40 nu = 0.436748 obj = -6.295220, rho = -0.055762 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 49 nu = 0.396994 obj = -7.216674, rho = -0.040782 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 61 nu = 0.354115 obj = -8.239346, rho = -0.034120 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.317421 obj = -9.419307, rho = -0.080793 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 63 nu = 0.283819 obj = -10.812235, rho = -0.104767 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.254545 obj = -12.421223, rho = -0.154203 nSV = 30, nBSV = 21 Total nSV = 30 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) *..* optimization finished, #iter = 205 nu = 0.228460 obj = -14.315994, rho = -0.205158 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 96.8% (968/1000) (classification) *..* optimization finished, #iter = 204 nu = 0.206988 obj = -16.503850, rho = -0.211506 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 96% (96/100) (classification) Accuracy = 97% (970/1000) (classification) .*...* optimization finished, #iter = 454 nu = 0.185109 obj = -19.047714, rho = -0.164662 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 96.8% (968/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.163993 obj = -22.180552, rho = -0.197586 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 141 nu = 0.148320 obj = -25.982976, rho = -0.273546 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 88 nu = 0.136838 obj = -30.599140, rho = -0.261755 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 99 nu = 0.127396 obj = -35.881908, rho = -0.299219 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*.* optimization finished, #iter = 263 nu = 0.115127 obj = -42.105271, rho = -0.331392 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 96.1% (961/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.106155 obj = -49.631004, rho = -0.463275 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 95.9% (959/1000) (classification) .* optimization finished, #iter = 145 nu = 0.101162 obj = -58.281266, rho = -0.645661 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 95.6% (956/1000) (classification) .* optimization finished, #iter = 144 nu = 0.092816 obj = -68.026782, rho = -0.766113 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 49 nu = 0.555064 obj = -3.794189, rho = -0.324144 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 37 nu = 0.493569 obj = -4.316449, rho = -0.314063 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.447795 obj = -4.920352, rho = -0.313741 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.401096 obj = -5.575150, rho = -0.313536 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 88 nu = 0.357608 obj = -6.284617, rho = -0.305501 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 56 nu = 0.314968 obj = -7.120497, rho = -0.281743 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 32 nu = 0.277316 obj = -8.083931, rho = -0.254448 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 28 nu = 0.250250 obj = -9.177571, rho = -0.171936 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 33 nu = 0.223046 obj = -10.384448, rho = -0.234731 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.197144 obj = -11.759335, rho = -0.225005 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 59 nu = 0.182279 obj = -13.235635, rho = -0.121529 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 80 nu = 0.160455 obj = -14.684702, rho = -0.063186 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 98 nu = 0.140936 obj = -16.285994, rho = -0.031600 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 136 nu = 0.120849 obj = -18.059214, rho = -0.017885 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 137 nu = 0.109525 obj = -19.805941, rho = -0.124859 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*...* optimization finished, #iter = 414 nu = 0.094797 obj = -21.562237, rho = -0.241027 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 156 nu = 0.079858 obj = -23.429055, rho = -0.254974 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 214 nu = 0.069305 obj = -25.404565, rho = -0.359892 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 140 nu = 0.058990 obj = -27.534628, rho = -0.387463 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.053332 obj = -29.319908, rho = -0.374477 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 37 nu = 0.550332 obj = -3.743954, rho = -0.130319 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 32 nu = 0.494352 obj = -4.262169, rho = -0.124090 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 35 nu = 0.443365 obj = -4.836255, rho = -0.142308 nSV = 47, nBSV = 43 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.405395 obj = -5.426520, rho = -0.133300 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 45 nu = 0.361815 obj = -6.056621, rho = -0.122989 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.327402 obj = -6.655911, rho = -0.176498 nSV = 34, nBSV = 30 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 186 nu = 0.281697 obj = -7.196647, rho = -0.167611 nSV = 33, nBSV = 23 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .**..* optimization finished, #iter = 362 nu = 0.235887 obj = -7.822479, rho = -0.159143 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *..* optimization finished, #iter = 226 nu = 0.202180 obj = -8.531424, rho = -0.122609 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 263 nu = 0.174597 obj = -9.256931, rho = -0.150436 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 230 nu = 0.149087 obj = -9.992088, rho = -0.176353 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.125497 obj = -10.818406, rho = -0.169640 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.111106 obj = -11.572390, rho = -0.144930 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.*.* optimization finished, #iter = 172 nu = 0.094169 obj = -12.205543, rho = -0.167261 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*......* optimization finished, #iter = 797 nu = 0.077041 obj = -12.810899, rho = -0.164838 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *..* optimization finished, #iter = 211 nu = 0.063300 obj = -13.527568, rho = -0.167226 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 164 nu = 0.052106 obj = -14.306482, rho = -0.161761 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 77 nu = 0.044629 obj = -15.023847, rho = -0.062307 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 158 nu = 0.036848 obj = -15.581759, rho = -0.010563 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ....*...* optimization finished, #iter = 736 nu = 0.030629 obj = -16.006712, rho = 0.023885 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 42 nu = 0.557124 obj = -3.691643, rho = 0.141454 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 56 nu = 0.491420 obj = -4.157995, rho = 0.202285 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 40 nu = 0.437837 obj = -4.686251, rho = 0.190961 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 59 nu = 0.381500 obj = -5.287544, rho = 0.191640 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 58 nu = 0.333152 obj = -5.995792, rho = 0.209431 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 27 nu = 0.300000 obj = -6.833938, rho = 0.125437 nSV = 32, nBSV = 28 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 56 nu = 0.268469 obj = -7.737472, rho = 0.040927 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 33 nu = 0.239732 obj = -8.771143, rho = 0.127376 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 45 nu = 0.210163 obj = -9.933067, rho = 0.236391 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 33 nu = 0.189221 obj = -11.310702, rho = 0.242955 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 84 nu = 0.167163 obj = -12.848812, rho = 0.292740 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 70 nu = 0.149566 obj = -14.598483, rho = 0.246529 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 90 nu = 0.132988 obj = -16.610333, rho = 0.190098 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 80 nu = 0.118267 obj = -18.927039, rho = 0.177383 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 84 nu = 0.104945 obj = -21.617395, rho = 0.217222 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 93 nu = 0.092593 obj = -24.806930, rho = 0.226074 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 68 nu = 0.082064 obj = -28.707044, rho = 0.183513 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 79 nu = 0.075182 obj = -33.398497, rho = 0.127374 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 92 nu = 0.069379 obj = -38.618550, rho = 0.081051 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 75 nu = 0.064053 obj = -44.411427, rho = -0.004213 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 44 nu = 0.563601 obj = -3.737545, rho = -0.220384 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 55 nu = 0.497939 obj = -4.206110, rho = -0.226753 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 42 nu = 0.453455 obj = -4.714860, rho = -0.168083 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 80 nu = 0.405242 obj = -5.219673, rho = -0.144214 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.346638 obj = -5.753767, rho = -0.127897 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 88 nu = 0.303447 obj = -6.340218, rho = -0.149220 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 73 nu = 0.265717 obj = -6.932005, rho = -0.226223 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 71 nu = 0.226830 obj = -7.533642, rho = -0.256616 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.194899 obj = -8.212087, rho = -0.259428 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 53 nu = 0.169140 obj = -8.901784, rho = -0.243623 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 183 nu = 0.145159 obj = -9.605884, rho = -0.248243 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 210 nu = 0.123280 obj = -10.335138, rho = -0.241629 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*...* optimization finished, #iter = 426 nu = 0.104989 obj = -11.018354, rho = -0.281799 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 144 nu = 0.087889 obj = -11.711470, rho = -0.295633 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..*...* optimization finished, #iter = 569 nu = 0.072003 obj = -12.472129, rho = -0.294583 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .**..* optimization finished, #iter = 304 nu = 0.059606 obj = -13.419140, rho = -0.297938 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *..* optimization finished, #iter = 206 nu = 0.051743 obj = -14.340491, rho = -0.316597 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.044458 obj = -15.139536, rho = -0.253770 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 98 nu = 0.038092 obj = -15.705921, rho = -0.232306 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 153 nu = 0.031597 obj = -15.797927, rho = -0.192431 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 37 nu = 0.570264 obj = -3.911676, rho = -0.190591 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 45 nu = 0.523390 obj = -4.440917, rho = -0.230854 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 59 nu = 0.468988 obj = -5.011586, rho = -0.256072 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.410600 obj = -5.631614, rho = -0.286544 nSV = 46, nBSV = 38 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 38 nu = 0.363603 obj = -6.363777, rho = -0.259878 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 81 nu = 0.321567 obj = -7.164780, rho = -0.187819 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 59 nu = 0.283611 obj = -8.093708, rho = -0.201487 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 79 nu = 0.248473 obj = -9.130353, rho = -0.186533 nSV = 30, nBSV = 21 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 50 nu = 0.217258 obj = -10.396038, rho = -0.148248 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 59 nu = 0.193216 obj = -11.893869, rho = -0.151638 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 53 nu = 0.180235 obj = -13.550191, rho = -0.160088 nSV = 20, nBSV = 16 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.158891 obj = -15.265201, rho = -0.142322 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 235 nu = 0.141324 obj = -17.182550, rho = -0.062485 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 163 nu = 0.123602 obj = -19.463024, rho = -0.045606 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.110057 obj = -22.122298, rho = -0.166287 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 187 nu = 0.095356 obj = -25.221951, rho = -0.152591 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 186 nu = 0.085368 obj = -28.986335, rho = 0.016640 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 139 nu = 0.078160 obj = -33.313473, rho = 0.100162 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 81 nu = 0.073343 obj = -37.824255, rho = 0.208232 nSV = 11, nBSV = 5 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) ...*....* optimization finished, #iter = 711 nu = 0.068385 obj = -41.761536, rho = 0.229327 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 52 nu = 0.569914 obj = -3.926804, rho = -0.153575 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 35 nu = 0.508820 obj = -4.490373, rho = -0.130984 nSV = 53, nBSV = 49 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 98 nu = 0.459875 obj = -5.124768, rho = -0.135965 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 33 nu = 0.415208 obj = -5.845741, rho = -0.123771 nSV = 43, nBSV = 40 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 33 nu = 0.369005 obj = -6.645057, rho = -0.117668 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 51 nu = 0.333339 obj = -7.542697, rho = -0.180100 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 59 nu = 0.300659 obj = -8.493340, rho = -0.203061 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.259857 obj = -9.601361, rho = -0.178457 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 34 nu = 0.234328 obj = -10.918765, rho = -0.292116 nSV = 26, nBSV = 21 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 86 nu = 0.212505 obj = -12.243079, rho = -0.453652 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 59 nu = 0.187271 obj = -13.710288, rho = -0.509071 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 50 nu = 0.170052 obj = -15.176844, rho = -0.499757 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 81 nu = 0.149276 obj = -16.582987, rho = -0.511908 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.129020 obj = -18.039155, rho = -0.507658 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *..* optimization finished, #iter = 270 nu = 0.107391 obj = -19.636227, rho = -0.525802 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 144 nu = 0.096577 obj = -21.417615, rho = -0.483966 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ...*.* optimization finished, #iter = 484 nu = 0.083584 obj = -22.616377, rho = -0.518842 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ...*.* optimization finished, #iter = 411 nu = 0.069315 obj = -23.866904, rho = -0.521178 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 232 nu = 0.058122 obj = -24.996312, rho = -0.532819 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *....* optimization finished, #iter = 455 nu = 0.047803 obj = -25.892157, rho = -0.505256 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 49 nu = 0.557894 obj = -3.813706, rho = -0.018627 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 40 nu = 0.500000 obj = -4.352733, rho = 0.030746 nSV = 51, nBSV = 49 Total nSV = 51 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 85 nu = 0.439596 obj = -4.962019, rho = 0.043947 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.389394 obj = -5.709007, rho = 0.088846 nSV = 45, nBSV = 36 Total nSV = 45 Accuracy = 96% (96/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 92 nu = 0.348989 obj = -6.585346, rho = 0.092592 nSV = 40, nBSV = 31 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 36 nu = 0.320000 obj = -7.633713, rho = 0.023517 nSV = 35, nBSV = 31 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 31 nu = 0.286980 obj = -8.803100, rho = 0.074389 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 55 nu = 0.257650 obj = -10.223248, rho = 0.091264 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 83 nu = 0.232676 obj = -11.922160, rho = 0.074960 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.209963 obj = -13.951041, rho = 0.068217 nSV = 28, nBSV = 17 Total nSV = 28 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 66 nu = 0.192783 obj = -16.433615, rho = -0.026654 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 72 nu = 0.179343 obj = -19.326653, rho = -0.160892 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 76 nu = 0.164614 obj = -22.719222, rho = -0.119197 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.154491 obj = -26.647679, rho = -0.186525 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 63 nu = 0.140091 obj = -31.281538, rho = -0.166686 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 76 nu = 0.131388 obj = -36.716325, rho = -0.233245 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 97% (97/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 121 nu = 0.119262 obj = -42.918296, rho = -0.354272 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 97% (97/100) (classification) Accuracy = 96% (960/1000) (classification) .*..* optimization finished, #iter = 323 nu = 0.108078 obj = -50.468782, rho = -0.485593 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 97% (97/100) (classification) Accuracy = 95.9% (959/1000) (classification) ..*.* optimization finished, #iter = 315 nu = 0.100670 obj = -59.579777, rho = -0.638167 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 95.6% (956/1000) (classification) ...* optimization finished, #iter = 378 nu = 0.093507 obj = -69.992406, rho = -0.625552 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 40 nu = 0.594075 obj = -3.932107, rho = -0.203632 nSV = 61, nBSV = 58 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 65 nu = 0.530456 obj = -4.402711, rho = -0.212716 nSV = 57, nBSV = 50 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 63 nu = 0.458403 obj = -4.939800, rho = -0.201436 nSV = 51, nBSV = 41 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 52 nu = 0.404274 obj = -5.572714, rho = -0.196092 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 51 nu = 0.356381 obj = -6.292945, rho = -0.235429 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 65 nu = 0.318396 obj = -7.097498, rho = -0.305125 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 63 nu = 0.285560 obj = -7.966894, rho = -0.244756 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.255459 obj = -8.890216, rho = -0.235559 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 154 nu = 0.222609 obj = -9.881479, rho = -0.239394 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *........* optimization finished, #iter = 875 nu = 0.192060 obj = -11.005317, rho = -0.215405 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.176348 obj = -12.239947, rho = -0.179289 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 186 nu = 0.152825 obj = -13.315437, rho = -0.179524 nSV = 22, nBSV = 10 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 272 nu = 0.128109 obj = -14.557650, rho = -0.166870 nSV = 20, nBSV = 9 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.109762 obj = -16.024007, rho = -0.171202 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 132 nu = 0.097757 obj = -17.516948, rho = -0.065410 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 79 nu = 0.085087 obj = -18.987011, rho = -0.116255 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 61 nu = 0.073055 obj = -20.279197, rho = -0.095293 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.061228 obj = -21.585952, rho = -0.137747 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.054021 obj = -22.610580, rho = -0.191636 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 151 nu = 0.045724 obj = -22.865139, rho = -0.218465 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 55 nu = 0.553785 obj = -3.729135, rho = -0.114417 nSV = 60, nBSV = 52 Total nSV = 60 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 37 nu = 0.488309 obj = -4.236873, rho = -0.124649 nSV = 51, nBSV = 48 Total nSV = 51 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 39 nu = 0.434573 obj = -4.809676, rho = -0.155422 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 60 nu = 0.382914 obj = -5.476776, rho = -0.144247 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 40 nu = 0.351344 obj = -6.246087, rho = -0.186326 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.306335 obj = -7.115806, rho = -0.157572 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 51 nu = 0.275081 obj = -8.128218, rho = -0.115830 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 28 nu = 0.249061 obj = -9.289826, rho = -0.070512 nSV = 26, nBSV = 22 Total nSV = 26 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.231554 obj = -10.490637, rho = 0.061776 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 95 nu = 0.203478 obj = -11.740795, rho = 0.108680 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.179799 obj = -13.148721, rho = 0.183726 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.153368 obj = -14.757099, rho = 0.200533 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.135350 obj = -16.713758, rho = 0.271515 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 75 nu = 0.119595 obj = -19.017039, rho = 0.255612 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 154 nu = 0.107777 obj = -21.526304, rho = 0.284781 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) ...*.* optimization finished, #iter = 451 nu = 0.094076 obj = -24.404361, rho = 0.330154 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..* optimization finished, #iter = 282 nu = 0.083704 obj = -27.918389, rho = 0.449830 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 132 nu = 0.073854 obj = -32.022592, rho = 0.502218 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 204 nu = 0.068888 obj = -36.686418, rho = 0.742259 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 186 nu = 0.059813 obj = -41.935359, rho = 0.765440 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 36 nu = 0.595728 obj = -4.190301, rho = -0.220492 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 42 nu = 0.543496 obj = -4.801175, rho = -0.283962 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 35 nu = 0.498255 obj = -5.477752, rho = -0.288558 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 58 nu = 0.440137 obj = -6.218283, rho = -0.289597 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 85 nu = 0.392161 obj = -7.080092, rho = -0.223196 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.350752 obj = -8.076064, rho = -0.213266 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 64 nu = 0.315068 obj = -9.203350, rho = -0.194001 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 55 nu = 0.282242 obj = -10.458795, rho = -0.180823 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.253760 obj = -11.858466, rho = -0.097020 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.224923 obj = -13.451673, rho = -0.080157 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 78 nu = 0.200567 obj = -15.221594, rho = -0.092670 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 66 nu = 0.178136 obj = -17.279279, rho = -0.050538 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 143 nu = 0.161180 obj = -19.485410, rho = -0.057604 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.142198 obj = -21.981252, rho = -0.083810 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.125294 obj = -24.755161, rho = -0.217685 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 93 nu = 0.111040 obj = -27.874881, rho = -0.376359 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 69 nu = 0.098975 obj = -31.366673, rho = -0.414586 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.088530 obj = -34.927628, rho = -0.445633 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 162 nu = 0.079036 obj = -38.668656, rho = -0.476137 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 152 nu = 0.071100 obj = -42.469604, rho = -0.482314 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 45 nu = 0.562608 obj = -3.743884, rho = -0.067327 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 59 nu = 0.490697 obj = -4.240462, rho = -0.039582 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 31 nu = 0.438526 obj = -4.824615, rho = -0.074738 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 35 nu = 0.392793 obj = -5.466906, rho = -0.089320 nSV = 41, nBSV = 37 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.352134 obj = -6.167222, rho = -0.064206 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 45 nu = 0.308296 obj = -6.983945, rho = -0.120777 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 30 nu = 0.277826 obj = -7.870571, rho = -0.096120 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.248198 obj = -8.849222, rho = -0.026689 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 54 nu = 0.215333 obj = -9.951003, rho = -0.013983 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.191608 obj = -11.184173, rho = -0.015415 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 89 nu = 0.170389 obj = -12.553666, rho = -0.089083 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 62 nu = 0.151105 obj = -14.071969, rho = -0.050347 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 54 nu = 0.134799 obj = -15.660764, rho = 0.007534 nSV = 15, nBSV = 11 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 99 nu = 0.121287 obj = -17.136304, rho = 0.045618 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 161 nu = 0.105340 obj = -18.563643, rho = -0.027129 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.087723 obj = -20.088543, rho = -0.020978 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 66 nu = 0.073548 obj = -21.930568, rho = 0.070237 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 64 nu = 0.063451 obj = -24.115042, rho = 0.223032 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 77 nu = 0.058413 obj = -26.021732, rho = 0.429711 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.051667 obj = -26.827016, rho = 0.497942 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 42 nu = 0.565112 obj = -3.814692, rho = -0.269307 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 43 nu = 0.501692 obj = -4.326617, rho = -0.250298 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 38 nu = 0.449463 obj = -4.908697, rho = -0.254315 nSV = 47, nBSV = 43 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 76 nu = 0.409820 obj = -5.508662, rho = -0.257059 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 87 nu = 0.354875 obj = -6.185338, rho = -0.251963 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 52 nu = 0.309395 obj = -6.996124, rho = -0.286314 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 76 nu = 0.273038 obj = -7.933090, rho = -0.326649 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 85 nu = 0.243075 obj = -9.017585, rho = -0.270789 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 89 nu = 0.213408 obj = -10.284100, rho = -0.254055 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 58 nu = 0.189659 obj = -11.831616, rho = -0.211881 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 60 nu = 0.175157 obj = -13.568607, rho = -0.229836 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 73 nu = 0.160959 obj = -15.330968, rho = -0.246369 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 57 nu = 0.142139 obj = -17.312072, rho = -0.258503 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.124985 obj = -19.551034, rho = -0.319935 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 138 nu = 0.111612 obj = -22.094123, rho = -0.460829 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 171 nu = 0.100364 obj = -24.715846, rho = -0.622412 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 190 nu = 0.086644 obj = -27.797634, rho = -0.671080 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 172 nu = 0.080000 obj = -31.088502, rho = -0.850814 nSV = 11, nBSV = 5 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..* optimization finished, #iter = 291 nu = 0.075483 obj = -33.590473, rho = -1.115873 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 182 nu = 0.064629 obj = -35.324006, rho = -1.142327 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 37 nu = 0.619498 obj = -4.424323, rho = -0.314347 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 95% (95/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 39 nu = 0.558740 obj = -5.127464, rho = -0.320809 nSV = 60, nBSV = 53 Total nSV = 60 Accuracy = 95% (95/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 46 nu = 0.505359 obj = -5.949090, rho = -0.313535 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 96% (96/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 42 nu = 0.460729 obj = -6.890944, rho = -0.305240 nSV = 50, nBSV = 43 Total nSV = 50 Accuracy = 96% (96/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 68 nu = 0.421413 obj = -7.990884, rho = -0.255246 nSV = 47, nBSV = 39 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 73 nu = 0.382057 obj = -9.276795, rho = -0.227521 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 37 nu = 0.345874 obj = -10.786282, rho = -0.222227 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 63 nu = 0.317589 obj = -12.568349, rho = -0.202453 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 42 nu = 0.289555 obj = -14.596618, rho = -0.199732 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 73 nu = 0.266355 obj = -16.937336, rho = -0.203793 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *..* optimization finished, #iter = 223 nu = 0.248461 obj = -19.548864, rho = -0.186010 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 90 nu = 0.229608 obj = -22.309553, rho = -0.228634 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 181 nu = 0.205234 obj = -25.187883, rho = -0.287020 nSV = 25, nBSV = 14 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.176415 obj = -28.678812, rho = -0.289321 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 96 nu = 0.156730 obj = -33.039994, rho = -0.339760 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 95 nu = 0.143784 obj = -37.999789, rho = -0.461097 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 178 nu = 0.133667 obj = -43.329486, rho = -0.530929 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 165 nu = 0.118575 obj = -48.994494, rho = -0.530274 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) .*.* optimization finished, #iter = 245 nu = 0.105563 obj = -55.484509, rho = -0.494499 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) *..* optimization finished, #iter = 297 nu = 0.095155 obj = -62.535765, rho = -0.392840 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 35 nu = 0.511872 obj = -3.380814, rho = -0.183850 nSV = 53, nBSV = 49 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 87 nu = 0.447940 obj = -3.803449, rho = -0.221464 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 43 nu = 0.391152 obj = -4.308021, rho = -0.255734 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 61 nu = 0.351833 obj = -4.892714, rho = -0.254821 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 86 nu = 0.313126 obj = -5.547430, rho = -0.297339 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 37 nu = 0.276028 obj = -6.296980, rho = -0.288017 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 32 nu = 0.251592 obj = -7.108101, rho = -0.305631 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 91 nu = 0.217644 obj = -8.009953, rho = -0.330304 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 29 nu = 0.193228 obj = -9.104455, rho = -0.339422 nSV = 22, nBSV = 17 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 37 nu = 0.173011 obj = -10.318169, rho = -0.355780 nSV = 20, nBSV = 15 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 64 nu = 0.158837 obj = -11.610447, rho = -0.338267 nSV = 18, nBSV = 13 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 46 nu = 0.141310 obj = -12.871064, rho = -0.314617 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 68 nu = 0.124866 obj = -14.281165, rho = -0.251611 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 74 nu = 0.108826 obj = -15.675452, rho = -0.273124 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 94 nu = 0.092459 obj = -17.253458, rho = -0.346952 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.081304 obj = -18.923626, rho = -0.410859 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 201 nu = 0.068589 obj = -20.754486, rho = -0.365335 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 131 nu = 0.058886 obj = -22.977186, rho = -0.387741 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.052071 obj = -25.369040, rho = -0.458445 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.046483 obj = -27.663493, rho = -0.421272 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 38 nu = 0.523434 obj = -3.609032, rho = -0.061150 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 38 nu = 0.475953 obj = -4.110218, rho = -0.025719 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 43 nu = 0.422849 obj = -4.662059, rho = -0.000408 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 56 nu = 0.378411 obj = -5.298749, rho = -0.024365 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 35 nu = 0.345392 obj = -5.992253, rho = 0.050701 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 57 nu = 0.311913 obj = -6.705341, rho = 0.082758 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 29 nu = 0.278822 obj = -7.411139, rho = 0.111274 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 87 nu = 0.245675 obj = -8.072636, rho = 0.147487 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 74 nu = 0.206909 obj = -8.771453, rho = 0.148850 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 66 nu = 0.175732 obj = -9.577751, rho = 0.138424 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 57 nu = 0.151292 obj = -10.487712, rho = 0.129464 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.128302 obj = -11.499724, rho = 0.050531 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) .* optimization finished, #iter = 179 nu = 0.113040 obj = -12.566674, rho = 0.044700 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 244 nu = 0.095875 obj = -13.728922, rho = 0.059482 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 51 nu = 0.081685 obj = -15.017230, rho = 0.136629 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.072125 obj = -16.260966, rho = 0.125759 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.060429 obj = -17.617941, rho = 0.144088 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 79 nu = 0.051203 obj = -19.175308, rho = 0.176225 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 80 nu = 0.044520 obj = -20.852429, rho = 0.419139 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.038163 obj = -22.444558, rho = 0.549802 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 50 nu = 0.612824 obj = -4.192241, rho = -0.124144 nSV = 65, nBSV = 58 Total nSV = 65 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 36 nu = 0.545579 obj = -4.778119, rho = -0.084866 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 36 nu = 0.487397 obj = -5.461630, rho = -0.112422 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 29 nu = 0.440000 obj = -6.236784, rho = -0.087968 nSV = 45, nBSV = 42 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 60 nu = 0.399946 obj = -7.079066, rho = -0.097880 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 42 nu = 0.357126 obj = -8.006077, rho = -0.109839 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 61 nu = 0.314856 obj = -9.041857, rho = -0.096773 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 62 nu = 0.281204 obj = -10.226164, rho = -0.127388 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 65 nu = 0.245298 obj = -11.577851, rho = -0.100898 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 81 nu = 0.220220 obj = -13.103354, rho = -0.176839 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 66 nu = 0.198734 obj = -14.750231, rho = -0.032948 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 65 nu = 0.175005 obj = -16.613843, rho = -0.094573 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 75 nu = 0.156651 obj = -18.586578, rho = -0.167591 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 46 nu = 0.141506 obj = -20.730025, rho = -0.040892 nSV = 16, nBSV = 11 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 62 nu = 0.133059 obj = -22.373451, rho = 0.006947 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 164 nu = 0.112106 obj = -23.380362, rho = 0.024696 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 76 nu = 0.092026 obj = -24.476071, rho = -0.009651 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 236 nu = 0.078117 obj = -25.221850, rho = -0.036292 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 184 nu = 0.062913 obj = -25.772475, rho = -0.038502 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.052175 obj = -26.088863, rho = -0.131386 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 38 nu = 0.514063 obj = -3.570960, rho = -0.093095 nSV = 56, nBSV = 50 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 28 nu = 0.477988 obj = -4.066125, rho = -0.099324 nSV = 48, nBSV = 45 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 56 nu = 0.427062 obj = -4.581675, rho = -0.105065 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 38 nu = 0.373911 obj = -5.161563, rho = -0.141555 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 33 nu = 0.333899 obj = -5.829211, rho = -0.111560 nSV = 35, nBSV = 32 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 38 nu = 0.298920 obj = -6.548098, rho = -0.105806 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 36 nu = 0.263544 obj = -7.312639, rho = -0.054093 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 76 nu = 0.233246 obj = -8.140335, rho = -0.030740 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.201066 obj = -9.072098, rho = -0.050840 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.176907 obj = -10.138851, rho = -0.024658 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 150 nu = 0.157072 obj = -11.235669, rho = 0.014042 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 74 nu = 0.135375 obj = -12.460937, rho = -0.005372 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 90 nu = 0.117757 obj = -13.841115, rho = -0.048941 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 87 nu = 0.103183 obj = -15.445744, rho = 0.031130 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.091623 obj = -17.095034, rho = 0.092038 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 88 nu = 0.081438 obj = -18.766882, rho = 0.129606 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 136 nu = 0.072456 obj = -20.203709, rho = 0.149275 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 203 nu = 0.060918 obj = -21.498198, rho = 0.253836 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.051178 obj = -22.844947, rho = 0.399461 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 216 nu = 0.043153 obj = -24.113537, rho = 0.554276 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 54 nu = 0.556010 obj = -3.636193, rho = -0.180173 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 49 nu = 0.496185 obj = -4.054403, rho = -0.220046 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 50 nu = 0.436107 obj = -4.499720, rho = -0.234554 nSV = 47, nBSV = 38 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 45 nu = 0.372619 obj = -5.007282, rho = -0.250042 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 77 nu = 0.333281 obj = -5.552176, rho = -0.204515 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 57 nu = 0.288805 obj = -6.151837, rho = -0.196857 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 85 nu = 0.247947 obj = -6.834417, rho = -0.179237 nSV = 30, nBSV = 21 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 63 nu = 0.224691 obj = -7.574289, rho = -0.116423 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 99.3% (993/1000) (classification) * optimization finished, #iter = 75 nu = 0.193712 obj = -8.297722, rho = -0.127384 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 99.4% (994/1000) (classification) * optimization finished, #iter = 66 nu = 0.168825 obj = -9.083194, rho = -0.166241 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 99.4% (994/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.142815 obj = -9.908267, rho = -0.161797 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 99.4% (994/1000) (classification) .* optimization finished, #iter = 157 nu = 0.125224 obj = -10.784421, rho = -0.025021 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 70 nu = 0.107394 obj = -11.661813, rho = 0.003064 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 99.3% (993/1000) (classification) * optimization finished, #iter = 72 nu = 0.091495 obj = -12.569580, rho = 0.002964 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 99.3% (993/1000) (classification) * optimization finished, #iter = 97 nu = 0.077100 obj = -13.551626, rho = -0.040296 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 99.3% (993/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.066522 obj = -14.517579, rho = -0.039376 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) .* optimization finished, #iter = 188 nu = 0.056239 obj = -15.413529, rho = -0.020294 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.048785 obj = -16.053037, rho = 0.075419 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 228 nu = 0.041426 obj = -16.300394, rho = 0.185934 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..*.* optimization finished, #iter = 318 nu = 0.032599 obj = -16.301623, rho = 0.178321 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 35 nu = 0.591149 obj = -3.867382, rho = -0.021154 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 46 nu = 0.523721 obj = -4.324568, rho = -0.004828 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 76 nu = 0.461159 obj = -4.827086, rho = -0.055773 nSV = 51, nBSV = 43 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 88 nu = 0.406101 obj = -5.376766, rho = -0.081655 nSV = 44, nBSV = 36 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 43 nu = 0.355128 obj = -5.993506, rho = -0.095202 nSV = 37, nBSV = 33 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.310769 obj = -6.641652, rho = -0.136776 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 37 nu = 0.272763 obj = -7.357645, rho = -0.072170 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 45 nu = 0.240394 obj = -8.102961, rho = -0.097316 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 83 nu = 0.206352 obj = -8.905843, rho = -0.142362 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 92 nu = 0.179623 obj = -9.737320, rho = -0.095857 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 75 nu = 0.152308 obj = -10.673631, rho = -0.114266 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*..* optimization finished, #iter = 375 nu = 0.129216 obj = -11.747776, rho = -0.122762 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 164 nu = 0.112262 obj = -13.030527, rho = -0.139484 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 140 nu = 0.096395 obj = -14.475162, rho = -0.143865 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 92 nu = 0.086825 obj = -16.099900, rho = -0.164993 nSV = 11, nBSV = 6 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.078930 obj = -17.393311, rho = -0.231011 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) ..*..* optimization finished, #iter = 421 nu = 0.065890 obj = -18.561924, rho = -0.266519 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 295 nu = 0.054055 obj = -20.008198, rho = -0.266415 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 135 nu = 0.045344 obj = -21.831388, rho = -0.239774 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 89 nu = 0.040062 obj = -23.736601, rho = -0.162420 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 53 nu = 0.580123 obj = -3.981928, rho = -0.091532 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 44 nu = 0.523194 obj = -4.531940, rho = -0.120740 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 39 nu = 0.470424 obj = -5.156971, rho = -0.131780 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 42 nu = 0.423332 obj = -5.814837, rho = -0.080217 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 78 nu = 0.368819 obj = -6.572955, rho = -0.119460 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 49 nu = 0.336384 obj = -7.398599, rho = -0.221468 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 73 nu = 0.299627 obj = -8.285555, rho = -0.227123 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 60 nu = 0.263463 obj = -9.233263, rho = -0.220368 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 153 nu = 0.233136 obj = -10.245209, rho = -0.136088 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 79 nu = 0.199267 obj = -11.375666, rho = -0.125457 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 181 nu = 0.177526 obj = -12.653376, rho = -0.174140 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*....* optimization finished, #iter = 502 nu = 0.153131 obj = -13.971503, rho = -0.218216 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 291 nu = 0.131935 obj = -15.532898, rho = -0.226096 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *....* optimization finished, #iter = 476 nu = 0.112668 obj = -17.352778, rho = -0.238115 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.096335 obj = -19.651826, rho = -0.233205 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.086413 obj = -22.413588, rho = -0.108911 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 174 nu = 0.076851 obj = -25.631800, rho = -0.106151 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 130 nu = 0.070566 obj = -29.100619, rho = -0.102725 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.064534 obj = -32.729195, rho = -0.439257 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 163 nu = 0.057666 obj = -36.385120, rho = -0.544023 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 34 nu = 0.536861 obj = -3.618247, rho = -0.099761 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 32 nu = 0.480749 obj = -4.092757, rho = -0.075555 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 28 nu = 0.428217 obj = -4.623518, rho = -0.108945 nSV = 45, nBSV = 41 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 57 nu = 0.381004 obj = -5.209242, rho = -0.134489 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 49 nu = 0.331954 obj = -5.883037, rho = -0.104539 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 48 nu = 0.297967 obj = -6.663105, rho = -0.139517 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 68 nu = 0.263910 obj = -7.504097, rho = -0.134660 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 70 nu = 0.233351 obj = -8.451200, rho = -0.082018 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 49 nu = 0.203574 obj = -9.563534, rho = -0.089232 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 28 nu = 0.184770 obj = -10.776290, rho = -0.010144 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 53 nu = 0.168212 obj = -12.020107, rho = -0.047183 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 257 nu = 0.143837 obj = -13.354546, rho = -0.043836 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 94 nu = 0.127527 obj = -14.867791, rho = 0.040068 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.108885 obj = -16.564306, rho = 0.053781 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.098702 obj = -18.424255, rho = 0.028380 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..*..* optimization finished, #iter = 441 nu = 0.089533 obj = -19.992445, rho = 0.059636 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ......*....* optimization finished, #iter = 1000 nu = 0.075427 obj = -21.472824, rho = 0.067705 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *......* optimization finished, #iter = 662 nu = 0.062077 obj = -23.251607, rho = 0.067611 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.051882 obj = -25.508874, rho = 0.075200 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.046148 obj = -28.090190, rho = 0.065881 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 36 nu = 0.580000 obj = -4.025667, rho = -0.164251 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 43 nu = 0.513702 obj = -4.618804, rho = -0.178086 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 37 nu = 0.460977 obj = -5.321064, rho = -0.142825 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 35 nu = 0.424970 obj = -6.128123, rho = -0.234816 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 40 nu = 0.379362 obj = -7.039474, rho = -0.308516 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 39 nu = 0.347926 obj = -8.084536, rho = -0.304777 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 41 nu = 0.313565 obj = -9.256447, rho = -0.266454 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.283009 obj = -10.581106, rho = -0.240610 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 51 nu = 0.260000 obj = -12.059465, rho = -0.172784 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 77 nu = 0.228324 obj = -13.646549, rho = -0.176088 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 43 nu = 0.206193 obj = -15.454234, rho = -0.190995 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 236 nu = 0.184973 obj = -17.315240, rho = -0.275795 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.162386 obj = -19.381639, rho = -0.270038 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 130 nu = 0.141811 obj = -21.696735, rho = -0.252491 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.122025 obj = -24.466345, rho = -0.217350 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.106125 obj = -27.860448, rho = -0.175561 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.093650 obj = -32.035293, rho = -0.155586 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.085571 obj = -36.915752, rho = -0.044239 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 199 nu = 0.079356 obj = -42.127315, rho = 0.042236 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..*..* optimization finished, #iter = 430 nu = 0.070244 obj = -47.769970, rho = 0.060780 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 29 nu = 0.538882 obj = -3.798254, rho = -0.266307 nSV = 54, nBSV = 52 Total nSV = 54 Accuracy = 96% (96/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 34 nu = 0.488405 obj = -4.362979, rho = -0.213204 nSV = 51, nBSV = 48 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 30 nu = 0.441325 obj = -5.003782, rho = -0.156687 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 42 nu = 0.400373 obj = -5.720144, rho = -0.208993 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 35 nu = 0.360560 obj = -6.531920, rho = -0.187516 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 51 nu = 0.321164 obj = -7.440124, rho = -0.076223 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 66 nu = 0.287723 obj = -8.506435, rho = -0.022618 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 55 nu = 0.258547 obj = -9.732265, rho = -0.013354 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 44 nu = 0.242942 obj = -11.051740, rho = 0.083961 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 65 nu = 0.212409 obj = -12.373317, rho = 0.106647 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 55 nu = 0.187636 obj = -13.918193, rho = 0.221637 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 75 nu = 0.169114 obj = -15.540753, rho = 0.407243 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 91 nu = 0.150997 obj = -17.214957, rho = 0.437644 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ..*.* optimization finished, #iter = 326 nu = 0.133708 obj = -18.740569, rho = 0.456202 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*.* optimization finished, #iter = 249 nu = 0.112428 obj = -20.414868, rho = 0.504208 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) .*..* optimization finished, #iter = 326 nu = 0.098391 obj = -22.085525, rho = 0.613894 nSV = 16, nBSV = 4 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) .* optimization finished, #iter = 137 nu = 0.083396 obj = -23.921368, rho = 0.656119 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 140 nu = 0.073514 obj = -25.440484, rho = 0.869607 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .*.* optimization finished, #iter = 265 nu = 0.063167 obj = -26.484245, rho = 1.069826 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) ......* optimization finished, #iter = 697 nu = 0.050530 obj = -27.421352, rho = 1.070847 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 35 nu = 0.499051 obj = -3.398643, rho = -0.110436 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 29 nu = 0.440650 obj = -3.874096, rho = -0.136525 nSV = 46, nBSV = 43 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.393863 obj = -4.420160, rho = -0.186558 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 0.350249 obj = -5.062858, rho = -0.224822 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 33 nu = 0.316071 obj = -5.828155, rho = -0.175445 nSV = 34, nBSV = 30 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 51 nu = 0.286154 obj = -6.679877, rho = -0.220514 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 51 nu = 0.254062 obj = -7.674682, rho = -0.150829 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 68 nu = 0.228523 obj = -8.852340, rho = -0.142400 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 60 nu = 0.206769 obj = -10.209284, rho = -0.134556 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 34 nu = 0.186121 obj = -11.830729, rho = -0.061595 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 48 nu = 0.176113 obj = -13.585572, rho = -0.052126 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 69 nu = 0.161513 obj = -15.373388, rho = -0.037617 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 159 nu = 0.143691 obj = -17.250608, rho = -0.081692 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.124646 obj = -19.431411, rho = -0.072770 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 70 nu = 0.111517 obj = -21.903759, rho = -0.049999 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 187 nu = 0.097594 obj = -24.645220, rho = -0.107321 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) ..*.* optimization finished, #iter = 391 nu = 0.087618 obj = -27.754685, rho = -0.115667 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*..* optimization finished, #iter = 482 nu = 0.076156 obj = -31.234542, rho = -0.126475 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*..* optimization finished, #iter = 439 nu = 0.066891 obj = -35.322062, rho = -0.160458 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 80 nu = 0.061803 obj = -39.767877, rho = -0.093057 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 36 nu = 0.537643 obj = -3.538450, rho = -0.263710 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.483813 obj = -3.965026, rho = -0.195956 nSV = 51, nBSV = 44 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 38 nu = 0.420434 obj = -4.427827, rho = -0.160013 nSV = 44, nBSV = 41 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 35 nu = 0.369472 obj = -4.935652, rho = -0.156102 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 49 nu = 0.331646 obj = -5.468347, rho = -0.148111 nSV = 35, nBSV = 31 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 81 nu = 0.288942 obj = -6.003178, rho = -0.169789 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 71 nu = 0.250646 obj = -6.578112, rho = -0.223069 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.220478 obj = -7.154981, rho = -0.269858 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 72 nu = 0.186392 obj = -7.732402, rho = -0.281705 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.156934 obj = -8.399921, rho = -0.293441 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 138 nu = 0.135736 obj = -9.093827, rho = -0.326638 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *..............* optimization finished, #iter = 1406 nu = 0.115722 obj = -9.820919, rho = -0.315291 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 159 nu = 0.097014 obj = -10.609596, rho = -0.294880 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *..* optimization finished, #iter = 252 nu = 0.081581 obj = -11.479384, rho = -0.273370 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 83 nu = 0.070840 obj = -12.432825, rho = -0.274478 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.062730 obj = -13.216024, rho = -0.207391 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 67 nu = 0.052490 obj = -13.877635, rho = -0.081150 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 68 nu = 0.045019 obj = -14.244003, rho = 0.007949 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 83 nu = 0.036391 obj = -14.278993, rho = 0.009167 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 83 nu = 0.028559 obj = -14.278993, rho = 0.009167 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 48 nu = 0.569677 obj = -3.957187, rho = 0.066468 nSV = 59, nBSV = 56 Total nSV = 59 Accuracy = 95% (95/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 36 nu = 0.509321 obj = -4.534183, rho = 0.053561 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 97% (97/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 39 nu = 0.459028 obj = -5.209425, rho = 0.007078 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 47 nu = 0.410962 obj = -5.987282, rho = -0.031548 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 96% (96/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 85 nu = 0.365026 obj = -6.903261, rho = -0.014745 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 96% (96/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 33 nu = 0.328142 obj = -8.025965, rho = 0.026250 nSV = 35, nBSV = 30 Total nSV = 35 Accuracy = 96% (96/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 55 nu = 0.300000 obj = -9.339558, rho = 0.049825 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 96% (96/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 60 nu = 0.272755 obj = -10.871726, rho = 0.052552 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 38 nu = 0.250958 obj = -12.649301, rho = 0.170021 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 40 nu = 0.232345 obj = -14.672041, rho = 0.083796 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 89 nu = 0.210659 obj = -16.903140, rho = 0.175817 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 130 nu = 0.189896 obj = -19.570037, rho = 0.225451 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.169254 obj = -22.757125, rho = 0.223586 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 46 nu = 0.161505 obj = -26.293667, rho = 0.326141 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 62 nu = 0.147807 obj = -30.044548, rho = 0.357007 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 89 nu = 0.134793 obj = -34.216759, rho = 0.208995 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.121168 obj = -38.349539, rho = 0.117632 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 186 nu = 0.108918 obj = -43.000414, rho = 0.221017 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 137 nu = 0.094466 obj = -48.013715, rho = 0.234992 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*..* optimization finished, #iter = 301 nu = 0.087777 obj = -52.854403, rho = 0.327800 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 49 nu = 0.576189 obj = -3.818817, rho = -0.321357 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.503409 obj = -4.308205, rho = -0.345143 nSV = 54, nBSV = 47 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 35 nu = 0.443808 obj = -4.887216, rho = -0.341395 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 47 nu = 0.395452 obj = -5.545226, rho = -0.267687 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 59 nu = 0.352987 obj = -6.285930, rho = -0.276113 nSV = 38, nBSV = 33 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 65 nu = 0.313216 obj = -7.135999, rho = -0.295427 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 53 nu = 0.276267 obj = -8.111148, rho = -0.271025 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 39 nu = 0.250439 obj = -9.210160, rho = -0.275009 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 38 nu = 0.223027 obj = -10.460768, rho = -0.350431 nSV = 25, nBSV = 21 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.199436 obj = -11.792477, rho = -0.315343 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.171718 obj = -13.402215, rho = -0.315736 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 52 nu = 0.159699 obj = -15.278590, rho = -0.175994 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 63 nu = 0.144541 obj = -17.226589, rho = -0.380927 nSV = 16, nBSV = 11 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.125361 obj = -19.296230, rho = -0.509028 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 153 nu = 0.108514 obj = -21.782709, rho = -0.577134 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 81 nu = 0.098107 obj = -24.640666, rho = -0.670776 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 89 nu = 0.089676 obj = -27.513243, rho = -0.782199 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.077000 obj = -30.491035, rho = -0.865649 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 130 nu = 0.065355 obj = -34.189367, rho = -0.879926 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.056218 obj = -38.843238, rho = -0.898004 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 82 nu = 0.574430 obj = -3.845759, rho = -0.178155 nSV = 61, nBSV = 54 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 63 nu = 0.504386 obj = -4.360169, rho = -0.192771 nSV = 55, nBSV = 48 Total nSV = 55 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 35 nu = 0.444297 obj = -4.957118, rho = -0.223499 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 36 nu = 0.394715 obj = -5.660127, rho = -0.174370 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.354686 obj = -6.472221, rho = -0.197257 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 49 nu = 0.320000 obj = -7.430787, rho = -0.175151 nSV = 35, nBSV = 30 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.286997 obj = -8.460062, rho = -0.162305 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 80 nu = 0.253511 obj = -9.699661, rho = -0.161774 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 176 nu = 0.228225 obj = -11.145837, rho = -0.120705 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 54 nu = 0.204714 obj = -12.836926, rho = -0.125573 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 55 nu = 0.187298 obj = -14.712473, rho = -0.229668 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.176673 obj = -16.739408, rho = -0.109839 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.159353 obj = -18.727378, rho = -0.058848 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 72 nu = 0.136655 obj = -20.900590, rho = -0.076153 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*....* optimization finished, #iter = 533 nu = 0.123520 obj = -23.258099, rho = -0.077952 nSV = 18, nBSV = 7 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) ..*.* optimization finished, #iter = 347 nu = 0.106425 obj = -25.765122, rho = -0.004791 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 158 nu = 0.094587 obj = -28.475882, rho = 0.195817 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ...* optimization finished, #iter = 364 nu = 0.083533 obj = -31.146658, rho = 0.438565 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .........*..* optimization finished, #iter = 1112 nu = 0.069871 obj = -34.047209, rho = 0.457100 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ....*....*.* optimization finished, #iter = 930 nu = 0.059461 obj = -37.465037, rho = 0.472146 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 48 nu = 0.578679 obj = -3.876789, rho = -0.433274 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 59 nu = 0.510370 obj = -4.391997, rho = -0.418131 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 39 nu = 0.459403 obj = -4.979394, rho = -0.419062 nSV = 47, nBSV = 43 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 35 nu = 0.405938 obj = -5.639805, rho = -0.387805 nSV = 43, nBSV = 39 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.364076 obj = -6.351829, rho = -0.391272 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 85 nu = 0.317626 obj = -7.161000, rho = -0.395919 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.293137 obj = -8.064644, rho = -0.436845 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *..* optimization finished, #iter = 236 nu = 0.256475 obj = -8.936862, rho = -0.441338 nSV = 31, nBSV = 21 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 78 nu = 0.220692 obj = -9.959001, rho = -0.412129 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.196180 obj = -11.106304, rho = -0.408619 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.170989 obj = -12.367789, rho = -0.413841 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 69 nu = 0.149628 obj = -13.711621, rho = -0.459768 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *..* optimization finished, #iter = 254 nu = 0.129666 obj = -15.263875, rho = -0.517718 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*..* optimization finished, #iter = 307 nu = 0.114113 obj = -16.933138, rho = -0.538124 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *......* optimization finished, #iter = 637 nu = 0.098365 obj = -18.798525, rho = -0.566741 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..* optimization finished, #iter = 252 nu = 0.083600 obj = -21.069135, rho = -0.594742 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 236 nu = 0.073247 obj = -23.836538, rho = -0.669412 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 92 nu = 0.065001 obj = -27.051725, rho = -0.754083 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.059948 obj = -30.499314, rho = -0.878448 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 192 nu = 0.056124 obj = -33.565634, rho = -1.005003 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 35 nu = 0.549627 obj = -3.777147, rho = -0.085926 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 59 nu = 0.509698 obj = -4.280828, rho = -0.036631 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.444445 obj = -4.823331, rho = -0.027074 nSV = 48, nBSV = 40 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 81 nu = 0.398178 obj = -5.449614, rho = -0.005682 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 59 nu = 0.360334 obj = -6.100473, rho = -0.025613 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 68 nu = 0.314303 obj = -6.797581, rho = -0.054794 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.273697 obj = -7.569089, rho = -0.119399 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 64 nu = 0.244154 obj = -8.419293, rho = -0.174718 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 59 nu = 0.217034 obj = -9.268629, rho = -0.179159 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 141 nu = 0.189984 obj = -10.108786, rho = -0.052799 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.162410 obj = -10.960629, rho = -0.053963 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 237 nu = 0.136431 obj = -11.880762, rho = -0.055453 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 76 nu = 0.116583 obj = -12.960278, rho = -0.094590 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 185 nu = 0.100165 obj = -14.068423, rho = -0.140182 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.087079 obj = -15.234064, rho = -0.101670 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.072910 obj = -16.393191, rho = -0.088531 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.062586 obj = -17.671556, rho = -0.114438 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 125 nu = 0.053000 obj = -18.902940, rho = -0.146290 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 147 nu = 0.044632 obj = -20.162752, rho = -0.136561 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 206 nu = 0.036683 obj = -21.575300, rho = -0.151650 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 35 nu = 0.594729 obj = -4.009903, rho = -0.124777 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 45 nu = 0.525138 obj = -4.554705, rho = -0.105006 nSV = 56, nBSV = 50 Total nSV = 56 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 34 nu = 0.480000 obj = -5.171259, rho = -0.054197 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 57 nu = 0.424297 obj = -5.836173, rho = -0.087518 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 47 nu = 0.367533 obj = -6.613481, rho = -0.084837 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 39 nu = 0.326288 obj = -7.548072, rho = -0.028982 nSV = 35, nBSV = 31 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 47 nu = 0.291349 obj = -8.604410, rho = 0.004057 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.266210 obj = -9.783888, rho = -0.064049 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 58 nu = 0.234954 obj = -11.097505, rho = -0.096187 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 83 nu = 0.211995 obj = -12.573169, rho = -0.209319 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 89 nu = 0.187378 obj = -14.212110, rho = -0.232986 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.166064 obj = -16.119546, rho = -0.158127 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 278 nu = 0.146207 obj = -18.341566, rho = -0.167431 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.130783 obj = -20.929987, rho = -0.177912 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 170 nu = 0.120323 obj = -23.706407, rho = -0.225027 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 71 nu = 0.106149 obj = -26.752095, rho = -0.116296 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 127 nu = 0.092721 obj = -30.239017, rho = -0.048399 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.081742 obj = -34.348406, rho = -0.015820 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 96 nu = 0.076713 obj = -38.784582, rho = 0.000489 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 292 nu = 0.067484 obj = -43.063200, rho = 0.164215 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 44 nu = 0.517383 obj = -3.521816, rho = -0.351605 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 32 nu = 0.467848 obj = -3.994377, rho = -0.305701 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 42 nu = 0.414721 obj = -4.519870, rho = -0.320263 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 73 nu = 0.366253 obj = -5.111770, rho = -0.306330 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 42 nu = 0.328009 obj = -5.790526, rho = -0.287761 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 43 nu = 0.289513 obj = -6.564211, rho = -0.327097 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 62 nu = 0.252126 obj = -7.468571, rho = -0.304393 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 98 nu = 0.225240 obj = -8.570959, rho = -0.296922 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 82 nu = 0.203248 obj = -9.853076, rho = -0.284754 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 73 nu = 0.181533 obj = -11.294873, rho = -0.297556 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.162584 obj = -13.001786, rho = -0.272995 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 75 nu = 0.149604 obj = -14.966937, rho = -0.232113 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 245 nu = 0.136703 obj = -17.084990, rho = -0.295935 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.120434 obj = -19.506453, rho = -0.306137 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.109456 obj = -22.200829, rho = -0.375690 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 194 nu = 0.095914 obj = -25.364339, rho = -0.375794 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 95.9% (959/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.087816 obj = -29.094914, rho = -0.435050 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 95.8% (958/1000) (classification) .* optimization finished, #iter = 126 nu = 0.082779 obj = -32.819615, rho = -0.575392 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 95.9% (959/1000) (classification) ...*.* optimization finished, #iter = 433 nu = 0.075821 obj = -36.276101, rho = -0.732572 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 94.8% (948/1000) (classification) .*.* optimization finished, #iter = 274 nu = 0.064311 obj = -39.695357, rho = -0.731753 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 32 nu = 0.498331 obj = -3.425552, rho = -0.433278 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 41 nu = 0.457745 obj = -3.891643, rho = -0.405674 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 44 nu = 0.400000 obj = -4.409778, rho = -0.398122 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 42 nu = 0.364936 obj = -4.985902, rho = -0.342056 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 28 nu = 0.319873 obj = -5.617207, rho = -0.288572 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 36 nu = 0.290220 obj = -6.327028, rho = -0.312457 nSV = 31, nBSV = 27 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 36 nu = 0.254885 obj = -7.070066, rho = -0.385401 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 38 nu = 0.225637 obj = -7.870452, rho = -0.331667 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 56 nu = 0.196814 obj = -8.736400, rho = -0.339923 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.170166 obj = -9.729383, rho = -0.388652 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 173 nu = 0.146079 obj = -10.906028, rho = -0.433884 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.128859 obj = -12.282567, rho = -0.458904 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 55 nu = 0.112332 obj = -13.895287, rho = -0.482575 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 251 nu = 0.097120 obj = -15.852795, rho = -0.490379 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 132 nu = 0.087997 obj = -18.216215, rho = -0.548974 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 108 nu = 0.081725 obj = -20.755142, rho = -0.610235 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 147 nu = 0.075346 obj = -23.227588, rho = -0.665781 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.067571 obj = -25.646708, rho = -0.628145 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.060634 obj = -27.838673, rho = -0.639384 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 82 nu = 0.054973 obj = -29.397088, rho = -0.534093 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 49 nu = 0.547892 obj = -3.684602, rho = -0.336724 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 39 nu = 0.492174 obj = -4.166681, rho = -0.332487 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 45 nu = 0.426252 obj = -4.719598, rho = -0.324654 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 41 nu = 0.383718 obj = -5.368540, rho = -0.404730 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 41 nu = 0.336353 obj = -6.118994, rho = -0.422871 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 47 nu = 0.304883 obj = -6.982659, rho = -0.340581 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 38 nu = 0.272518 obj = -7.947212, rho = -0.253195 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 52 nu = 0.246243 obj = -9.000521, rho = -0.186429 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 176 nu = 0.218284 obj = -10.181932, rho = -0.183353 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.190336 obj = -11.554211, rho = -0.159797 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.167204 obj = -13.229909, rho = -0.208563 nSV = 22, nBSV = 11 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 98 nu = 0.146604 obj = -15.303912, rho = -0.235704 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.131577 obj = -17.862327, rho = -0.310312 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.119873 obj = -20.916458, rho = -0.404754 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.109876 obj = -24.605071, rho = -0.481240 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 81 nu = 0.102665 obj = -28.893592, rho = -0.591849 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 97% (97/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 77 nu = 0.099634 obj = -33.522208, rho = -0.781577 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 69 nu = 0.094567 obj = -37.836958, rho = -0.967423 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 84 nu = 0.086007 obj = -42.095501, rho = -1.179513 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ..*..* optimization finished, #iter = 492 nu = 0.078490 obj = -45.608647, rho = -1.445440 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 37 nu = 0.564228 obj = -3.839366, rho = -0.091072 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 41 nu = 0.514640 obj = -4.338540, rho = -0.063794 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 38 nu = 0.466372 obj = -4.843006, rho = -0.045655 nSV = 49, nBSV = 42 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 44 nu = 0.403627 obj = -5.404191, rho = -0.047491 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 46 nu = 0.355174 obj = -6.023687, rho = -0.092252 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 67 nu = 0.314981 obj = -6.712721, rho = -0.074308 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 61 nu = 0.270358 obj = -7.449614, rho = -0.052526 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 77 nu = 0.239073 obj = -8.305247, rho = -0.114079 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 76 nu = 0.205951 obj = -9.240627, rho = -0.138840 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.177084 obj = -10.352638, rho = -0.164155 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.153314 obj = -11.696004, rho = -0.172236 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *..* optimization finished, #iter = 254 nu = 0.137636 obj = -13.240014, rho = -0.198782 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 146 nu = 0.122980 obj = -14.979354, rho = -0.180554 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 140 nu = 0.108472 obj = -16.897475, rho = -0.168149 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 130 nu = 0.094412 obj = -19.096110, rho = -0.149323 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.081697 obj = -21.842636, rho = -0.131859 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.072231 obj = -25.242581, rho = -0.168511 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 229 nu = 0.063936 obj = -29.490236, rho = -0.203423 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.058728 obj = -34.685602, rho = -0.319086 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.055101 obj = -40.671868, rho = -0.452960 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 49 nu = 0.613804 obj = -4.144025, rho = -0.159534 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 59 nu = 0.548938 obj = -4.692114, rho = -0.136599 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 68 nu = 0.482084 obj = -5.320383, rho = -0.138491 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.424484 obj = -6.070496, rho = -0.148947 nSV = 44, nBSV = 41 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 40 nu = 0.381290 obj = -6.929664, rho = -0.112912 nSV = 40, nBSV = 36 Total nSV = 40 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.346372 obj = -7.873387, rho = -0.013514 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 31 nu = 0.314473 obj = -8.920790, rho = -0.076276 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 88 nu = 0.281080 obj = -9.981794, rho = -0.135980 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 141 nu = 0.248856 obj = -11.152276, rho = -0.189955 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 164 nu = 0.215302 obj = -12.457246, rho = -0.215852 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.188049 obj = -13.968918, rho = -0.251470 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 78 nu = 0.163214 obj = -15.730024, rho = -0.232408 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.143180 obj = -17.787011, rho = -0.276479 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.131237 obj = -20.128287, rho = -0.330598 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 151 nu = 0.118987 obj = -22.387557, rho = -0.446118 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) *...* optimization finished, #iter = 315 nu = 0.101759 obj = -24.903451, rho = -0.438211 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 146 nu = 0.087906 obj = -27.891099, rho = -0.427393 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.076892 obj = -31.357442, rho = -0.319193 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.067558 obj = -35.362302, rho = -0.249628 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 72 nu = 0.062016 obj = -39.679028, rho = -0.403309 nSV = 9, nBSV = 3 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 35 nu = 0.603630 obj = -4.066132, rho = -0.142982 nSV = 62, nBSV = 60 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 53 nu = 0.540683 obj = -4.589365, rho = -0.124470 nSV = 57, nBSV = 50 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 45 nu = 0.478787 obj = -5.192574, rho = -0.109805 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 57 nu = 0.421053 obj = -5.876467, rho = -0.081659 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 60 nu = 0.378034 obj = -6.644752, rho = -0.110475 nSV = 42, nBSV = 34 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 87 nu = 0.332628 obj = -7.495762, rho = -0.099686 nSV = 38, nBSV = 30 Total nSV = 38 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 125 nu = 0.289934 obj = -8.518650, rho = -0.117218 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 81 nu = 0.261998 obj = -9.713860, rho = -0.098968 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 66 nu = 0.233765 obj = -11.037320, rho = -0.145864 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 79 nu = 0.209183 obj = -12.532516, rho = -0.220545 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 76 nu = 0.188017 obj = -14.246257, rho = -0.251532 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *..* optimization finished, #iter = 204 nu = 0.167932 obj = -16.062931, rho = -0.261816 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.145542 obj = -18.205961, rho = -0.209217 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 207 nu = 0.134118 obj = -20.587453, rho = -0.198540 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 157 nu = 0.116376 obj = -23.153768, rho = -0.262710 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 224 nu = 0.101957 obj = -26.218155, rho = -0.301504 nSV = 18, nBSV = 7 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 150 nu = 0.088728 obj = -29.967580, rho = -0.315968 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 134 nu = 0.081981 obj = -34.289226, rho = -0.358164 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.076963 obj = -38.421186, rho = -0.551619 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 89 nu = 0.070690 obj = -42.072123, rho = -0.742265 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 34 nu = 0.496110 obj = -3.399580, rho = -0.008693 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 96% (96/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 39 nu = 0.455385 obj = -3.859359, rho = -0.036192 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 36 nu = 0.405799 obj = -4.354551, rho = -0.057597 nSV = 42, nBSV = 39 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 76 nu = 0.356323 obj = -4.904744, rho = -0.080245 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 62 nu = 0.314338 obj = -5.545405, rho = -0.081753 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 75 nu = 0.276673 obj = -6.279994, rho = -0.056835 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 51 nu = 0.247663 obj = -7.135793, rho = -0.037224 nSV = 28, nBSV = 23 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 42 nu = 0.222214 obj = -8.063009, rho = -0.099183 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 46 nu = 0.192728 obj = -9.151219, rho = -0.068831 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.169487 obj = -10.442114, rho = -0.037750 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 188 nu = 0.151394 obj = -12.002511, rho = -0.036131 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.136598 obj = -13.754493, rho = -0.061211 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 67 nu = 0.120326 obj = -15.879397, rho = -0.039501 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 51 nu = 0.110524 obj = -18.391380, rho = 0.060057 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 62 nu = 0.099313 obj = -21.302356, rho = 0.018460 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 96 nu = 0.090120 obj = -24.736938, rho = 0.003339 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 212 nu = 0.083475 obj = -28.672944, rho = 0.096329 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.074785 obj = -33.202746, rho = 0.155563 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.067905 obj = -38.536393, rho = 0.221948 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 166 nu = 0.062494 obj = -44.680665, rho = 0.303373 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 33 nu = 0.504137 obj = -3.364042, rho = -0.183007 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 33 nu = 0.448872 obj = -3.794227, rho = -0.179611 nSV = 48, nBSV = 44 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 46 nu = 0.403832 obj = -4.248376, rho = -0.138724 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 50 nu = 0.349155 obj = -4.755603, rho = -0.142900 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 73 nu = 0.303703 obj = -5.365556, rho = -0.137473 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 95 nu = 0.274503 obj = -6.035957, rho = -0.205327 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.235379 obj = -6.811728, rho = -0.208402 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 89 nu = 0.211103 obj = -7.721303, rho = -0.218477 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 74 nu = 0.189945 obj = -8.703319, rho = -0.229638 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.168346 obj = -9.766851, rho = -0.212534 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 67 nu = 0.147389 obj = -11.000498, rho = -0.220910 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 96 nu = 0.130485 obj = -12.333552, rho = -0.247238 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 187 nu = 0.112613 obj = -13.913127, rho = -0.232923 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.098392 obj = -15.808678, rho = -0.272270 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 96 nu = 0.086049 obj = -18.151009, rho = -0.259434 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 88 nu = 0.078255 obj = -20.935561, rho = -0.321079 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 93 nu = 0.072058 obj = -24.018825, rho = -0.398931 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.067276 obj = -27.258241, rho = -0.500482 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 67 nu = 0.061097 obj = -30.425165, rho = -0.511224 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 50 nu = 0.056433 obj = -33.326877, rho = -0.555260 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 34 nu = 0.537054 obj = -3.619956, rho = -0.183939 nSV = 56, nBSV = 49 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 35 nu = 0.478964 obj = -4.109365, rho = -0.223672 nSV = 49, nBSV = 46 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 61 nu = 0.432345 obj = -4.625458, rho = -0.200215 nSV = 47, nBSV = 39 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 67 nu = 0.385264 obj = -5.177732, rho = -0.160247 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 76 nu = 0.348893 obj = -5.737862, rho = -0.089136 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 0.297439 obj = -6.329349, rho = -0.071361 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 64 nu = 0.255126 obj = -7.044973, rho = -0.050642 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 72 nu = 0.222399 obj = -7.866271, rho = -0.029442 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 78 nu = 0.200170 obj = -8.734328, rho = -0.010390 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 89 nu = 0.171354 obj = -9.670746, rho = -0.001754 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 162 nu = 0.149521 obj = -10.719764, rho = 0.015976 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 159 nu = 0.128064 obj = -11.943431, rho = -0.004550 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 200 nu = 0.111545 obj = -13.364743, rho = -0.020030 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 181 nu = 0.096209 obj = -15.033568, rho = -0.021945 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 91 nu = 0.082875 obj = -17.102378, rho = -0.023607 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 66 nu = 0.074332 obj = -19.642139, rho = -0.026559 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.068522 obj = -22.396664, rho = -0.029572 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.061475 obj = -25.330065, rho = -0.032269 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 73 nu = 0.056090 obj = -28.550523, rho = 0.074283 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 92 nu = 0.052584 obj = -31.456040, rho = 0.237616 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 32 nu = 0.521298 obj = -3.688901, rho = -0.185488 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 29 nu = 0.472219 obj = -4.256068, rho = -0.227096 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 38 nu = 0.425295 obj = -4.907377, rho = -0.209205 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 35 nu = 0.396578 obj = -5.648796, rho = -0.179281 nSV = 41, nBSV = 37 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 33 nu = 0.357458 obj = -6.447856, rho = -0.168044 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 65 nu = 0.321040 obj = -7.338673, rho = -0.140850 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 47 nu = 0.295327 obj = -8.310539, rho = -0.163777 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 86 nu = 0.260638 obj = -9.320867, rho = -0.189823 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.226890 obj = -10.456244, rho = -0.173219 nSV = 28, nBSV = 17 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 95 nu = 0.194620 obj = -11.858310, rho = -0.163229 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 61 nu = 0.172422 obj = -13.563295, rho = -0.158843 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.155294 obj = -15.510177, rho = -0.325505 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 98 nu = 0.137568 obj = -17.790568, rho = -0.388481 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *..* optimization finished, #iter = 270 nu = 0.121673 obj = -20.572994, rho = -0.411170 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.108765 obj = -24.008435, rho = -0.419921 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 77 nu = 0.102118 obj = -28.013883, rho = -0.584003 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 76 nu = 0.095873 obj = -32.301153, rho = -0.748490 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 92 nu = 0.090427 obj = -36.656284, rho = -0.724810 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 124 nu = 0.079376 obj = -41.186198, rho = -0.797477 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 153 nu = 0.069720 obj = -46.413826, rho = -0.930408 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.613915 obj = -4.094533, rho = 0.074346 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 42 nu = 0.542026 obj = -4.623316, rho = 0.061538 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 39 nu = 0.487547 obj = -5.230578, rho = 0.130449 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.434928 obj = -5.882465, rho = 0.205317 nSV = 45, nBSV = 41 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 48 nu = 0.389126 obj = -6.557976, rho = 0.191419 nSV = 42, nBSV = 34 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.336030 obj = -7.296644, rho = 0.186180 nSV = 37, nBSV = 27 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 67 nu = 0.290704 obj = -8.172737, rho = 0.216075 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.257610 obj = -9.181765, rho = 0.218328 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 84 nu = 0.227249 obj = -10.259865, rho = 0.202331 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.199353 obj = -11.437623, rho = 0.196307 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 70 nu = 0.179854 obj = -12.756342, rho = 0.179117 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 64 nu = 0.156481 obj = -13.994158, rho = 0.108484 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.134457 obj = -15.419317, rho = 0.139558 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 95 nu = 0.113634 obj = -17.095587, rho = 0.114528 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.098167 obj = -19.141954, rho = 0.076057 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 88 nu = 0.088184 obj = -21.400567, rho = 0.076677 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 73 nu = 0.077013 obj = -23.820676, rho = 0.117429 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 83 nu = 0.069871 obj = -26.274766, rho = 0.179768 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.065931 obj = -28.127057, rho = 0.182752 nSV = 11, nBSV = 5 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..*..* optimization finished, #iter = 445 nu = 0.055029 obj = -28.915821, rho = 0.150728 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 35 nu = 0.544330 obj = -3.622843, rho = -0.177094 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 36 nu = 0.489603 obj = -4.082028, rho = -0.131264 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 39 nu = 0.428187 obj = -4.590100, rho = -0.172540 nSV = 45, nBSV = 41 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 56 nu = 0.376414 obj = -5.163292, rho = -0.179673 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 43 nu = 0.326255 obj = -5.843070, rho = -0.171475 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 53 nu = 0.297071 obj = -6.612008, rho = -0.253207 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 47 nu = 0.265544 obj = -7.456844, rho = -0.312526 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.235138 obj = -8.359780, rho = -0.329415 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 34 nu = 0.209844 obj = -9.343744, rho = -0.320573 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 63 nu = 0.184311 obj = -10.384183, rho = -0.333385 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.162984 obj = -11.444809, rho = -0.247493 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 94 nu = 0.138490 obj = -12.651520, rho = -0.269926 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.119824 obj = -14.034094, rho = -0.322248 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.104931 obj = -15.606734, rho = -0.410727 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 162 nu = 0.095366 obj = -17.101039, rho = -0.579970 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 172 nu = 0.080688 obj = -18.609405, rho = -0.589235 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 47 nu = 0.069789 obj = -20.285333, rho = -0.558276 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.061918 obj = -21.804959, rho = -0.615565 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.052616 obj = -22.963358, rho = -0.623226 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.046167 obj = -23.740450, rho = -0.442200 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 35 nu = 0.495336 obj = -3.176254, rho = -0.075680 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 54 nu = 0.430670 obj = -3.539569, rho = -0.057868 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 35 nu = 0.375306 obj = -3.952643, rho = -0.074300 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 28 nu = 0.328533 obj = -4.416778, rho = -0.098247 nSV = 35, nBSV = 32 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 49 nu = 0.298035 obj = -4.887290, rho = -0.034045 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 57 nu = 0.257969 obj = -5.364693, rho = -0.025650 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 62 nu = 0.221344 obj = -5.900585, rho = 0.001442 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 40 nu = 0.193793 obj = -6.466153, rho = 0.030490 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.169965 obj = -7.000031, rho = -0.021133 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.141747 obj = -7.580096, rho = -0.007107 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 85 nu = 0.121684 obj = -8.204548, rho = 0.043859 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 56 nu = 0.104336 obj = -8.869539, rho = -0.016840 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 144 nu = 0.090277 obj = -9.461496, rho = -0.077282 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 177 nu = 0.076897 obj = -10.017557, rho = -0.048199 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*.* optimization finished, #iter = 278 nu = 0.066699 obj = -10.335205, rho = -0.022011 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*.* optimization finished, #iter = 298 nu = 0.052996 obj = -10.558624, rho = -0.010394 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.042944 obj = -10.797484, rho = 0.010666 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 187 nu = 0.035275 obj = -10.925444, rho = 0.048837 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) .*.* optimization finished, #iter = 268 nu = 0.027855 obj = -10.927159, rho = 0.053957 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) .*.* optimization finished, #iter = 268 nu = 0.021860 obj = -10.927159, rho = 0.053957 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 32 nu = 0.580000 obj = -4.027861, rho = 0.016201 nSV = 59, nBSV = 57 Total nSV = 59 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 34 nu = 0.522270 obj = -4.596950, rho = -0.027652 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.470950 obj = -5.250249, rho = 0.024993 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 63 nu = 0.426289 obj = -5.979155, rho = -0.048010 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 57 nu = 0.373005 obj = -6.819269, rho = -0.048266 nSV = 42, nBSV = 34 Total nSV = 42 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 31 nu = 0.338632 obj = -7.807185, rho = 0.016567 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 63 nu = 0.299538 obj = -8.918589, rho = 0.023125 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 23 nu = 0.278723 obj = -10.174906, rho = 0.087271 nSV = 29, nBSV = 25 Total nSV = 29 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.246409 obj = -11.441214, rho = 0.094942 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 272 nu = 0.214659 obj = -12.988398, rho = 0.116515 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.187058 obj = -14.870415, rho = 0.079556 nSV = 24, nBSV = 13 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 94 nu = 0.168400 obj = -17.189433, rho = 0.064696 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.151247 obj = -19.811816, rho = 0.079999 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 50 nu = 0.135114 obj = -22.991285, rho = 0.105362 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.122151 obj = -26.880623, rho = 0.121835 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 89 nu = 0.115791 obj = -31.237634, rho = 0.242957 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 85 nu = 0.105884 obj = -35.915265, rho = 0.404625 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.093389 obj = -41.494663, rho = 0.411531 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 151 nu = 0.084795 obj = -48.232086, rho = 0.477222 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) *.* optimization finished, #iter = 182 nu = 0.079335 obj = -55.828193, rho = 0.314272 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 47 nu = 0.566808 obj = -3.793859, rho = -0.143346 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 36 nu = 0.512511 obj = -4.258226, rho = -0.116079 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 39 nu = 0.452176 obj = -4.767819, rho = -0.163897 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 49 nu = 0.393637 obj = -5.322469, rho = -0.216650 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 33 nu = 0.349653 obj = -5.957328, rho = -0.193086 nSV = 37, nBSV = 33 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.308654 obj = -6.609143, rho = -0.193275 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.274322 obj = -7.322413, rho = -0.213044 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.235597 obj = -8.084595, rho = -0.217334 nSV = 28, nBSV = 18 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.201153 obj = -8.945984, rho = -0.205202 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 146 nu = 0.177619 obj = -9.918579, rho = -0.176298 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 157 nu = 0.155648 obj = -10.938624, rho = -0.172061 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.132575 obj = -12.080160, rho = -0.180897 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.116215 obj = -13.318360, rho = -0.155822 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 182 nu = 0.100641 obj = -14.692724, rho = -0.179576 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.088376 obj = -16.140281, rho = -0.102820 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 74 nu = 0.080872 obj = -17.452212, rho = -0.124528 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 255 nu = 0.072764 obj = -18.003345, rho = 0.135384 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 177 nu = 0.058437 obj = -18.110762, rho = 0.176242 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..* optimization finished, #iter = 288 nu = 0.046287 obj = -18.163017, rho = 0.206121 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..* optimization finished, #iter = 288 nu = 0.036323 obj = -18.163014, rho = 0.206012 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 47 nu = 0.563498 obj = -3.844728, rho = -0.194042 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 59 nu = 0.501582 obj = -4.368803, rho = -0.170685 nSV = 55, nBSV = 48 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 43 nu = 0.444577 obj = -4.988035, rho = -0.168688 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 44 nu = 0.396499 obj = -5.710151, rho = -0.174095 nSV = 44, nBSV = 37 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 34 nu = 0.359452 obj = -6.558843, rho = -0.242650 nSV = 38, nBSV = 34 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 29 nu = 0.320500 obj = -7.512483, rho = -0.253073 nSV = 34, nBSV = 30 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 43 nu = 0.288329 obj = -8.606062, rho = -0.236093 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 32 nu = 0.257087 obj = -9.895243, rho = -0.210377 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 68 nu = 0.230135 obj = -11.410826, rho = -0.182455 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 34 nu = 0.213043 obj = -13.171996, rho = -0.252853 nSV = 23, nBSV = 19 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 83 nu = 0.192667 obj = -15.053861, rho = -0.353067 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 185 nu = 0.170328 obj = -17.298214, rho = -0.314873 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.154217 obj = -19.956636, rho = -0.293845 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 64 nu = 0.141639 obj = -22.913656, rho = -0.271963 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.125434 obj = -26.237500, rho = -0.316384 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 157 nu = 0.110672 obj = -30.320782, rho = -0.382276 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*..* optimization finished, #iter = 319 nu = 0.101664 obj = -35.110723, rho = -0.385006 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.093636 obj = -40.415462, rho = -0.406392 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) ...*.* optimization finished, #iter = 439 nu = 0.081592 obj = -46.766503, rho = -0.408051 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.072329 obj = -54.845351, rho = -0.405722 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 39 nu = 0.545731 obj = -3.581439, rho = -0.006239 nSV = 57, nBSV = 51 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 40 nu = 0.477165 obj = -4.030506, rho = -0.001856 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 79 nu = 0.422805 obj = -4.537700, rho = -0.005944 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 76 nu = 0.371833 obj = -5.104341, rho = 0.038411 nSV = 41, nBSV = 33 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 68 nu = 0.324410 obj = -5.776845, rho = 0.005843 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 45 nu = 0.292798 obj = -6.537087, rho = -0.023937 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 48 nu = 0.261605 obj = -7.377431, rho = -0.077308 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 42 nu = 0.233789 obj = -8.276651, rho = -0.143653 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 59 nu = 0.204633 obj = -9.264027, rho = -0.154435 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.184779 obj = -10.265377, rho = -0.095046 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 176 nu = 0.160959 obj = -11.325523, rho = -0.067034 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 91 nu = 0.138954 obj = -12.479168, rho = 0.011300 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 121 nu = 0.118556 obj = -13.813205, rho = 0.015789 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.101588 obj = -15.375939, rho = 0.018238 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..*.* optimization finished, #iter = 335 nu = 0.087716 obj = -17.237259, rho = 0.018968 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..*..* optimization finished, #iter = 444 nu = 0.075625 obj = -19.465217, rho = 0.021195 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .*....* optimization finished, #iter = 515 nu = 0.065236 obj = -22.269296, rho = 0.027151 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 167 nu = 0.057553 obj = -25.803424, rho = 0.039311 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 90 nu = 0.051544 obj = -30.187462, rho = 0.040823 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.046776 obj = -35.498241, rho = 0.030798 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 68 nu = 0.626418 obj = -4.317367, rho = -0.043727 nSV = 67, nBSV = 60 Total nSV = 67 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 43 nu = 0.557691 obj = -4.938916, rho = -0.033811 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 55 nu = 0.506027 obj = -5.646445, rho = -0.096520 nSV = 54, nBSV = 47 Total nSV = 54 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 74 nu = 0.453753 obj = -6.432055, rho = -0.105093 nSV = 49, nBSV = 42 Total nSV = 49 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 46 nu = 0.409611 obj = -7.320200, rho = -0.106379 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 51 nu = 0.363068 obj = -8.311916, rho = -0.157328 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 96% (96/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 44 nu = 0.328599 obj = -9.451342, rho = -0.221137 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 76 nu = 0.291244 obj = -10.693117, rho = -0.267207 nSV = 34, nBSV = 25 Total nSV = 34 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 81 nu = 0.254109 obj = -12.159887, rho = -0.241963 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 87 nu = 0.227605 obj = -13.900207, rho = -0.244878 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 95 nu = 0.199634 obj = -15.944026, rho = -0.279239 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 91 nu = 0.177329 obj = -18.450701, rho = -0.323978 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 79 nu = 0.160691 obj = -21.474016, rho = -0.302327 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.144976 obj = -25.077258, rho = -0.240008 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.132802 obj = -29.377664, rho = -0.214317 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.123259 obj = -34.414806, rho = -0.308014 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.115224 obj = -40.149852, rho = -0.465735 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 161 nu = 0.106187 obj = -46.418397, rho = -0.546464 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 180 nu = 0.095133 obj = -53.678510, rho = -0.678062 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..* optimization finished, #iter = 264 nu = 0.086178 obj = -62.262813, rho = -0.748626 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 55 nu = 0.615527 obj = -4.226737, rho = -0.110133 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 38 nu = 0.555120 obj = -4.814261, rho = -0.124053 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 40 nu = 0.512557 obj = -5.450409, rho = -0.164137 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 50 nu = 0.450132 obj = -6.122162, rho = -0.162303 nSV = 47, nBSV = 43 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 70 nu = 0.392774 obj = -6.888991, rho = -0.196126 nSV = 42, nBSV = 34 Total nSV = 42 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.345040 obj = -7.787949, rho = -0.169314 nSV = 39, nBSV = 30 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 69 nu = 0.307482 obj = -8.830228, rho = -0.202689 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 81 nu = 0.273155 obj = -9.976426, rho = -0.139105 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.237227 obj = -11.323413, rho = -0.135752 nSV = 30, nBSV = 20 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 55 nu = 0.210468 obj = -12.926479, rho = -0.214702 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 53 nu = 0.192920 obj = -14.701527, rho = -0.208316 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 38 nu = 0.176198 obj = -16.660635, rho = -0.204891 nSV = 19, nBSV = 16 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.162684 obj = -18.459039, rho = -0.232124 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 239 nu = 0.138595 obj = -20.299062, rho = -0.213786 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.118884 obj = -22.430205, rho = -0.236568 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*..* optimization finished, #iter = 324 nu = 0.102992 obj = -24.890087, rho = -0.334225 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 158 nu = 0.088450 obj = -27.694286, rho = -0.400583 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 137 nu = 0.078161 obj = -31.016420, rho = -0.457286 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 151 nu = 0.070372 obj = -34.195233, rho = -0.476155 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 185 nu = 0.063332 obj = -37.185005, rho = -0.335411 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 37 nu = 0.546310 obj = -3.711355, rho = -0.250979 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 34 nu = 0.497200 obj = -4.198870, rho = -0.281830 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 32 nu = 0.444962 obj = -4.714429, rho = -0.273381 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 43 nu = 0.392505 obj = -5.271538, rho = -0.304629 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 57 nu = 0.344155 obj = -5.893746, rho = -0.336781 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 66 nu = 0.298683 obj = -6.616003, rho = -0.322378 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 79 nu = 0.260671 obj = -7.434638, rho = -0.305705 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.228929 obj = -8.412228, rho = -0.329584 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 84 nu = 0.207892 obj = -9.482810, rho = -0.336919 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 195 nu = 0.180310 obj = -10.656233, rho = -0.332690 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 65 nu = 0.157472 obj = -12.059015, rho = -0.348118 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 91 nu = 0.142476 obj = -13.681358, rho = -0.275061 nSV = 17, nBSV = 12 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 157 nu = 0.124948 obj = -15.446731, rho = -0.249206 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 66 nu = 0.109022 obj = -17.574656, rho = -0.246235 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 32 nu = 0.096879 obj = -20.162603, rho = -0.265268 nSV = 12, nBSV = 7 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 75 nu = 0.088929 obj = -22.989381, rho = -0.248948 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 78 nu = 0.081653 obj = -26.051491, rho = -0.341326 nSV = 10, nBSV = 4 Total nSV = 10 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.073487 obj = -29.137444, rho = -0.488957 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 91 nu = 0.066303 obj = -32.266858, rho = -0.672237 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*..* optimization finished, #iter = 310 nu = 0.059128 obj = -35.081936, rho = -0.759483 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 43 nu = 0.581328 obj = -3.948038, rho = 0.122581 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 68 nu = 0.525683 obj = -4.475486, rho = 0.082764 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 50 nu = 0.468783 obj = -5.058799, rho = 0.117588 nSV = 50, nBSV = 43 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 59 nu = 0.411809 obj = -5.720154, rho = 0.110794 nSV = 45, nBSV = 37 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 43 nu = 0.371798 obj = -6.464651, rho = 0.174381 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 88 nu = 0.325484 obj = -7.265818, rho = 0.216521 nSV = 38, nBSV = 30 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 87 nu = 0.287251 obj = -8.203787, rho = 0.210787 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 66 nu = 0.256974 obj = -9.262041, rho = 0.260394 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.225404 obj = -10.429899, rho = 0.297961 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 79 nu = 0.198496 obj = -11.794528, rho = 0.249208 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 48 nu = 0.178481 obj = -13.286872, rho = 0.229690 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 78 nu = 0.162157 obj = -14.861286, rho = 0.394434 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 61 nu = 0.148695 obj = -16.295586, rho = 0.515121 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.127495 obj = -17.529287, rho = 0.558059 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.110013 obj = -18.744787, rho = 0.647333 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 146 nu = 0.093796 obj = -19.809142, rho = 0.694823 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 169 nu = 0.082551 obj = -20.413528, rho = 0.746826 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ......* optimization finished, #iter = 666 nu = 0.066313 obj = -20.493011, rho = 0.735334 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .......*.* optimization finished, #iter = 809 nu = 0.052225 obj = -20.494595, rho = 0.736664 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .......*.* optimization finished, #iter = 809 nu = 0.040984 obj = -20.494595, rho = 0.736664 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 33 nu = 0.517748 obj = -3.540071, rho = -0.146332 nSV = 52, nBSV = 49 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 41 nu = 0.462304 obj = -4.034158, rho = -0.116044 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 34 nu = 0.420000 obj = -4.599806, rho = -0.102560 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 39 nu = 0.380100 obj = -5.190793, rho = -0.138705 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 44 nu = 0.338381 obj = -5.819363, rho = -0.192409 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.298615 obj = -6.498096, rho = -0.160206 nSV = 35, nBSV = 26 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*...* optimization finished, #iter = 463 nu = 0.261871 obj = -7.246622, rho = -0.171058 nSV = 32, nBSV = 21 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 63 nu = 0.224968 obj = -8.141778, rho = -0.165407 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 77 nu = 0.201702 obj = -9.118576, rho = -0.131870 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 61 nu = 0.181233 obj = -10.160888, rho = -0.138872 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.163702 obj = -11.110550, rho = -0.120756 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 175 nu = 0.138820 obj = -12.098342, rho = -0.103086 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 206 nu = 0.119586 obj = -13.175826, rho = -0.088073 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..* optimization finished, #iter = 241 nu = 0.100959 obj = -14.312866, rho = -0.043646 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 283 nu = 0.085682 obj = -15.651316, rho = -0.029222 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 192 nu = 0.072069 obj = -17.228185, rho = -0.054355 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 148 nu = 0.062598 obj = -19.061371, rho = -0.160257 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 71 nu = 0.056192 obj = -20.958210, rho = -0.254134 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 130 nu = 0.050411 obj = -22.444011, rho = -0.300833 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..* optimization finished, #iter = 234 nu = 0.043255 obj = -23.600033, rho = -0.182264 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.573566 obj = -3.864749, rho = -0.228686 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 63 nu = 0.516168 obj = -4.362336, rho = -0.199572 nSV = 55, nBSV = 48 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 45 nu = 0.457929 obj = -4.921582, rho = -0.215995 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 48 nu = 0.401162 obj = -5.547182, rho = -0.214053 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 44 nu = 0.356507 obj = -6.261646, rho = -0.168098 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 51 nu = 0.321613 obj = -7.020660, rho = -0.159629 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 72 nu = 0.276918 obj = -7.882889, rho = -0.156151 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 34 nu = 0.242321 obj = -8.913983, rho = -0.194159 nSV = 27, nBSV = 23 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 39 nu = 0.215989 obj = -10.091587, rho = -0.237091 nSV = 24, nBSV = 20 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 44 nu = 0.196889 obj = -11.345410, rho = -0.286874 nSV = 22, nBSV = 17 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 43 nu = 0.174701 obj = -12.655976, rho = -0.354644 nSV = 20, nBSV = 15 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 45 nu = 0.151060 obj = -14.101394, rho = -0.355663 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 93 nu = 0.132377 obj = -15.797214, rho = -0.338459 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 58 nu = 0.120687 obj = -17.554227, rho = -0.308363 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 47 nu = 0.104129 obj = -19.307683, rho = -0.356789 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 87 nu = 0.089061 obj = -21.313320, rho = -0.387099 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) .*.* optimization finished, #iter = 220 nu = 0.077202 obj = -23.627417, rho = -0.493849 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 230 nu = 0.068081 obj = -26.157202, rho = -0.497346 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) ...*.* optimization finished, #iter = 424 nu = 0.058788 obj = -28.863603, rho = -0.459471 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) ...*.* optimization finished, #iter = 498 nu = 0.050616 obj = -31.837278, rho = -0.461315 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 32 nu = 0.569435 obj = -3.968381, rho = -0.044817 nSV = 58, nBSV = 55 Total nSV = 58 Accuracy = 97% (97/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 53 nu = 0.514889 obj = -4.537863, rho = -0.039855 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 97% (97/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 41 nu = 0.467352 obj = -5.182433, rho = -0.025612 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 76 nu = 0.417237 obj = -5.901317, rho = -0.019220 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 97% (97/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 32 nu = 0.368803 obj = -6.738048, rho = 0.003109 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 65 nu = 0.326733 obj = -7.723022, rho = 0.020830 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 40 nu = 0.290179 obj = -8.923189, rho = 0.006563 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 89 nu = 0.263780 obj = -10.323466, rho = 0.111560 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 51 nu = 0.235614 obj = -12.007315, rho = 0.065131 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 68 nu = 0.214040 obj = -13.998972, rho = 0.030597 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 81 nu = 0.190917 obj = -16.466206, rho = 0.037054 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 97% (97/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 84 nu = 0.178272 obj = -19.448258, rho = 0.128110 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 68 nu = 0.168566 obj = -22.865274, rho = 0.173465 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 97% (97/100) (classification) Accuracy = 96.3% (963/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.156575 obj = -26.657809, rho = 0.184314 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 97% (97/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*.* optimization finished, #iter = 226 nu = 0.140963 obj = -31.134924, rho = 0.142000 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 96% (96/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 222 nu = 0.125610 obj = -36.672695, rho = 0.141963 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.115615 obj = -43.630248, rho = 0.158264 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 138 nu = 0.106340 obj = -52.005058, rho = 0.201270 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 87 nu = 0.101140 obj = -62.057714, rho = 0.364441 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 97% (97/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 179 nu = 0.093602 obj = -74.004992, rho = 0.303234 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 74 nu = 0.602195 obj = -4.020593, rho = -0.038767 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 68 nu = 0.526861 obj = -4.558238, rho = -0.061747 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 62 nu = 0.468257 obj = -5.176765, rho = -0.051612 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 37 nu = 0.418797 obj = -5.897611, rho = -0.063288 nSV = 43, nBSV = 39 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 49 nu = 0.384712 obj = -6.680182, rho = -0.187654 nSV = 40, nBSV = 36 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 76 nu = 0.341804 obj = -7.496471, rho = -0.302838 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 60 nu = 0.296527 obj = -8.425847, rho = -0.292383 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 73 nu = 0.265069 obj = -9.478146, rho = -0.281129 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.231598 obj = -10.626646, rho = -0.332877 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.199407 obj = -12.014059, rho = -0.359691 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 83 nu = 0.176775 obj = -13.720850, rho = -0.358687 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 74 nu = 0.157271 obj = -15.693646, rho = -0.370417 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 56 nu = 0.142124 obj = -17.952753, rho = -0.455776 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.128589 obj = -20.451290, rho = -0.468485 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.113615 obj = -23.297477, rho = -0.468591 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 170 nu = 0.104168 obj = -26.547244, rho = -0.540564 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 261 nu = 0.093688 obj = -30.025967, rho = -0.703444 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 234 nu = 0.083298 obj = -33.627522, rho = -0.865131 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..* optimization finished, #iter = 258 nu = 0.073329 obj = -37.841059, rho = -0.959936 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..*....* optimization finished, #iter = 674 nu = 0.064852 obj = -42.368718, rho = -1.027958 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 42 nu = 0.566083 obj = -3.834212, rho = -0.035369 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 96% (96/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 46 nu = 0.503767 obj = -4.346903, rho = -0.058145 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 96% (96/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 58 nu = 0.441609 obj = -4.953084, rho = -0.083965 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 96% (96/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 46 nu = 0.397483 obj = -5.664587, rho = -0.108291 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 96% (96/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 41 nu = 0.360229 obj = -6.457510, rho = -0.131659 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 96% (96/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.324120 obj = -7.335062, rho = -0.151300 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 96% (96/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 64 nu = 0.289220 obj = -8.320506, rho = -0.217042 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 67 nu = 0.259177 obj = -9.416904, rho = -0.259130 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 68 nu = 0.230376 obj = -10.628881, rho = -0.353131 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 171 nu = 0.200917 obj = -12.021816, rho = -0.387960 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 56 nu = 0.175819 obj = -13.707553, rho = -0.409134 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 121 nu = 0.158470 obj = -15.635326, rho = -0.446128 nSV = 18, nBSV = 13 Total nSV = 18 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 135 nu = 0.138644 obj = -17.889276, rho = -0.445048 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 47 nu = 0.124305 obj = -20.672284, rho = -0.538671 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.114241 obj = -23.798013, rho = -0.679672 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 57 nu = 0.105529 obj = -27.293878, rho = -0.700600 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *...* optimization finished, #iter = 303 nu = 0.100681 obj = -30.626148, rho = -0.730467 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *..* optimization finished, #iter = 283 nu = 0.086363 obj = -33.754687, rho = -0.721026 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 244 nu = 0.072843 obj = -37.681592, rho = -0.720697 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.065474 obj = -42.230893, rho = -0.949150 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 34 nu = 0.567145 obj = -3.959562, rho = -0.197230 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 39 nu = 0.519041 obj = -4.529565, rho = -0.191465 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 52 nu = 0.469146 obj = -5.136794, rho = -0.178738 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 39 nu = 0.420206 obj = -5.817202, rho = -0.183921 nSV = 45, nBSV = 39 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 32 nu = 0.371067 obj = -6.563489, rho = -0.141635 nSV = 39, nBSV = 34 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 51 nu = 0.323952 obj = -7.454470, rho = -0.149525 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 88 nu = 0.284981 obj = -8.514600, rho = -0.163270 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 42 nu = 0.260168 obj = -9.738787, rho = -0.109962 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 36 nu = 0.231819 obj = -11.147944, rho = -0.070553 nSV = 26, nBSV = 22 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 45 nu = 0.211229 obj = -12.729941, rho = -0.086982 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 87 nu = 0.191071 obj = -14.419763, rho = -0.173979 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 35 nu = 0.173225 obj = -16.245593, rho = -0.397535 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 54 nu = 0.152345 obj = -18.132988, rho = -0.477340 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.133230 obj = -20.219675, rho = -0.474522 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 83 nu = 0.116547 obj = -22.642842, rho = -0.483778 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.103274 obj = -25.298406, rho = -0.545159 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.089339 obj = -28.336481, rho = -0.630910 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 59 nu = 0.081390 obj = -31.676455, rho = -0.913567 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.074824 obj = -34.529803, rho = -0.997027 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) .*.* optimization finished, #iter = 296 nu = 0.064955 obj = -36.698630, rho = -1.016744 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 43 nu = 0.538805 obj = -3.694903, rho = -0.138162 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 34 nu = 0.486035 obj = -4.203968, rho = -0.100624 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 54 nu = 0.432198 obj = -4.772162, rho = -0.161642 nSV = 48, nBSV = 41 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 42 nu = 0.387262 obj = -5.420141, rho = -0.207928 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 38 nu = 0.344572 obj = -6.166350, rho = -0.203603 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 89 nu = 0.308191 obj = -6.996276, rho = -0.202125 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 63 nu = 0.268979 obj = -7.955890, rho = -0.226436 nSV = 32, nBSV = 22 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 41 nu = 0.239283 obj = -9.136966, rho = -0.208461 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 62 nu = 0.218169 obj = -10.467546, rho = -0.118304 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 65 nu = 0.192931 obj = -11.997582, rho = -0.076944 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 46 nu = 0.180784 obj = -13.686767, rho = -0.238831 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 0.163216 obj = -15.448354, rho = -0.266669 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 53 nu = 0.146977 obj = -17.283938, rho = -0.311099 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 166 nu = 0.131224 obj = -19.051729, rho = -0.331989 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*..* optimization finished, #iter = 306 nu = 0.112287 obj = -20.967426, rho = -0.234310 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 133 nu = 0.100485 obj = -23.049335, rho = -0.184895 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 130 nu = 0.091338 obj = -24.672141, rho = 0.009356 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 162 nu = 0.075251 obj = -25.857033, rho = 0.060876 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 165 nu = 0.060959 obj = -27.327573, rho = 0.038323 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 153 nu = 0.049868 obj = -29.192100, rho = 0.008188 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 47 nu = 0.627940 obj = -4.347471, rho = -0.161080 nSV = 65, nBSV = 59 Total nSV = 65 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 55 nu = 0.564164 obj = -4.977765, rho = -0.082392 nSV = 59, nBSV = 55 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 58 nu = 0.509814 obj = -5.688979, rho = -0.047460 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 36 nu = 0.464497 obj = -6.491075, rho = -0.047913 nSV = 48, nBSV = 44 Total nSV = 48 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 71 nu = 0.409948 obj = -7.348614, rho = -0.019722 nSV = 44, nBSV = 36 Total nSV = 44 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.359420 obj = -8.371300, rho = -0.015053 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 88 nu = 0.317892 obj = -9.623571, rho = -0.006690 nSV = 36, nBSV = 27 Total nSV = 36 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 71 nu = 0.284349 obj = -11.111639, rho = 0.023705 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 48 nu = 0.255324 obj = -12.888805, rho = 0.024910 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 58 nu = 0.232052 obj = -15.012044, rho = 0.110385 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 47 nu = 0.215046 obj = -17.478071, rho = 0.138119 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 61 nu = 0.199602 obj = -20.209539, rho = 0.179581 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 137 nu = 0.181267 obj = -23.185358, rho = 0.145754 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .**..* optimization finished, #iter = 278 nu = 0.162387 obj = -26.602038, rho = 0.091233 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 154 nu = 0.151634 obj = -30.354884, rho = 0.139937 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*...* optimization finished, #iter = 455 nu = 0.135737 obj = -34.193922, rho = 0.100669 nSV = 18, nBSV = 7 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.118050 obj = -38.766231, rho = 0.066458 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 143 nu = 0.106996 obj = -44.020835, rho = -0.046179 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 199 nu = 0.098272 obj = -49.415952, rho = -0.280879 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 246 nu = 0.087395 obj = -54.638594, rho = -0.239810 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 71 nu = 0.535411 obj = -3.587693, rho = 0.041154 nSV = 56, nBSV = 50 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 32 nu = 0.480000 obj = -4.055425, rho = 0.080161 nSV = 50, nBSV = 46 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 44 nu = 0.426672 obj = -4.563468, rho = 0.073980 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 33 nu = 0.372053 obj = -5.138464, rho = 0.085003 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 52 nu = 0.330967 obj = -5.799095, rho = 0.062407 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 43 nu = 0.299908 obj = -6.506342, rho = 0.028487 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 82 nu = 0.263104 obj = -7.262543, rho = 0.021686 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 64 nu = 0.237340 obj = -8.050131, rho = -0.006282 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 85 nu = 0.205590 obj = -8.812901, rho = -0.023744 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.178921 obj = -9.668172, rho = -0.062353 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 68 nu = 0.158623 obj = -10.461267, rho = -0.114396 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 71 nu = 0.135689 obj = -11.159047, rho = -0.140493 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 57 nu = 0.116288 obj = -11.814130, rho = -0.171043 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) ..*..* optimization finished, #iter = 444 nu = 0.095413 obj = -12.389757, rho = -0.168979 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 228 nu = 0.078545 obj = -12.975003, rho = -0.165611 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.066652 obj = -13.529972, rho = -0.121437 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 161 nu = 0.054491 obj = -13.902023, rho = -0.069005 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..* optimization finished, #iter = 265 nu = 0.044265 obj = -14.230354, rho = -0.042356 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 207 nu = 0.036109 obj = -14.477320, rho = -0.017603 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 163 nu = 0.029022 obj = -14.510169, rho = -0.004105 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 57 nu = 0.569636 obj = -3.980420, rho = -0.198231 nSV = 61, nBSV = 54 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.515137 obj = -4.564431, rho = -0.216237 nSV = 55, nBSV = 47 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 41 nu = 0.458027 obj = -5.247161, rho = -0.260616 nSV = 52, nBSV = 44 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 39 nu = 0.414891 obj = -6.037199, rho = -0.211783 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 43 nu = 0.374452 obj = -6.921120, rho = -0.189582 nSV = 42, nBSV = 35 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 57 nu = 0.338069 obj = -7.953861, rho = -0.283273 nSV = 38, nBSV = 30 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 83 nu = 0.307874 obj = -9.131796, rho = -0.264692 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.278275 obj = -10.412926, rho = -0.170949 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 56 nu = 0.246946 obj = -11.909237, rho = -0.178916 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.229274 obj = -13.509937, rho = -0.049857 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.200156 obj = -15.295020, rho = -0.074954 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 88 nu = 0.178652 obj = -17.344552, rho = -0.074197 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 67 nu = 0.161239 obj = -19.632460, rho = -0.055325 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 190 nu = 0.142211 obj = -22.107489, rho = -0.053521 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 146 nu = 0.127370 obj = -24.974666, rho = 0.082963 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 82 nu = 0.115311 obj = -27.978061, rho = 0.071596 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*..* optimization finished, #iter = 355 nu = 0.104086 obj = -30.701684, rho = -0.092376 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..*..* optimization finished, #iter = 418 nu = 0.088161 obj = -33.618409, rho = -0.149503 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 190 nu = 0.075088 obj = -37.023548, rho = -0.220339 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 96 nu = 0.068115 obj = -40.550812, rho = -0.576784 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 50 nu = 0.584450 obj = -3.945168, rho = -0.102143 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 48 nu = 0.516541 obj = -4.483526, rho = -0.107362 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 43 nu = 0.459768 obj = -5.117953, rho = -0.100313 nSV = 48, nBSV = 44 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 37 nu = 0.421144 obj = -5.809541, rho = -0.117100 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 56 nu = 0.376711 obj = -6.534596, rho = -0.110835 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 47 nu = 0.333539 obj = -7.358040, rho = -0.164522 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 65 nu = 0.297302 obj = -8.206137, rho = -0.247133 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 69 nu = 0.262723 obj = -9.115065, rho = -0.233236 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 68 nu = 0.229410 obj = -10.119641, rho = -0.297701 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 172 nu = 0.198296 obj = -11.185464, rho = -0.297653 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 157 nu = 0.170779 obj = -12.443338, rho = -0.373560 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 63 nu = 0.146405 obj = -13.946748, rho = -0.365039 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 50 nu = 0.136186 obj = -15.562041, rho = -0.209655 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 200 nu = 0.118811 obj = -17.000955, rho = -0.148948 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 64 nu = 0.101700 obj = -18.622055, rho = -0.171392 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.085524 obj = -20.453610, rho = -0.219277 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.073925 obj = -22.668296, rho = -0.261265 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.066972 obj = -24.928008, rho = -0.364366 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 167 nu = 0.059858 obj = -26.699843, rho = -0.465138 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 150 nu = 0.051179 obj = -28.081956, rho = -0.439802 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 38 nu = 0.530781 obj = -3.517628, rho = -0.030042 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 41 nu = 0.475080 obj = -3.964319, rho = -0.066812 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 92 nu = 0.414596 obj = -4.445726, rho = -0.068167 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 27 nu = 0.374179 obj = -4.995439, rho = -0.050090 nSV = 39, nBSV = 36 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 44 nu = 0.338641 obj = -5.514151, rho = -0.024511 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 54 nu = 0.291339 obj = -6.043687, rho = 0.001659 nSV = 34, nBSV = 25 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.255321 obj = -6.609678, rho = -0.003370 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 96 nu = 0.215570 obj = -7.191993, rho = -0.030000 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.184338 obj = -7.869416, rho = -0.067442 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 73 nu = 0.157330 obj = -8.630045, rho = -0.062645 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 44 nu = 0.138947 obj = -9.389547, rho = -0.067167 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 60 nu = 0.119560 obj = -10.151312, rho = -0.003782 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 62 nu = 0.101556 obj = -10.906372, rho = 0.024991 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 133 nu = 0.087553 obj = -11.676176, rho = 0.051656 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.072455 obj = -12.396454, rho = 0.078643 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 79 nu = 0.065233 obj = -12.966725, rho = -0.086347 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 57 nu = 0.052635 obj = -13.217299, rho = -0.077494 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .*.* optimization finished, #iter = 293 nu = 0.043262 obj = -13.321101, rho = -0.194026 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .*.* optimization finished, #iter = 293 nu = 0.033950 obj = -13.321101, rho = -0.194026 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .*.* optimization finished, #iter = 293 nu = 0.026643 obj = -13.321101, rho = -0.194026 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 43 nu = 0.527729 obj = -3.466182, rho = -0.069692 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 40 nu = 0.471519 obj = -3.882584, rho = -0.116229 nSV = 48, nBSV = 44 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.412531 obj = -4.329907, rho = -0.122722 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 64 nu = 0.362261 obj = -4.832793, rho = -0.115691 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.320004 obj = -5.369950, rho = -0.187626 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 32 nu = 0.280274 obj = -5.965263, rho = -0.225033 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 33 nu = 0.246832 obj = -6.587833, rho = -0.173602 nSV = 27, nBSV = 22 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 77 nu = 0.215793 obj = -7.205295, rho = -0.184526 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 42 nu = 0.186262 obj = -7.885865, rho = -0.170397 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 48 nu = 0.160495 obj = -8.559695, rho = -0.145720 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.137393 obj = -9.304105, rho = -0.119763 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 90 nu = 0.119132 obj = -10.021335, rho = -0.104138 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 239 nu = 0.098566 obj = -10.777071, rho = -0.115475 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 222 nu = 0.082171 obj = -11.693737, rho = -0.105601 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.070044 obj = -12.786718, rho = -0.004890 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.062532 obj = -13.891196, rho = 0.004977 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 218 nu = 0.055341 obj = -14.630750, rho = -0.002057 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 167 nu = 0.048672 obj = -14.988427, rho = 0.009545 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 167 nu = 0.038196 obj = -14.988427, rho = 0.009545 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 167 nu = 0.029974 obj = -14.988427, rho = 0.009545 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 48 nu = 0.565542 obj = -3.833628, rho = -0.181624 nSV = 61, nBSV = 53 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 88 nu = 0.505145 obj = -4.342317, rho = -0.157234 nSV = 55, nBSV = 46 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 64 nu = 0.450848 obj = -4.924783, rho = -0.138593 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 50 nu = 0.401392 obj = -5.578407, rho = -0.153160 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 30 nu = 0.360000 obj = -6.322601, rho = -0.124473 nSV = 37, nBSV = 34 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.318069 obj = -7.117788, rho = -0.086025 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 57 nu = 0.284691 obj = -8.027892, rho = -0.088661 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.255051 obj = -9.017376, rho = -0.145106 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 199 nu = 0.220398 obj = -10.104004, rho = -0.157653 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *..* optimization finished, #iter = 248 nu = 0.192165 obj = -11.406954, rho = -0.141251 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 82 nu = 0.169479 obj = -12.919412, rho = -0.131392 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 71 nu = 0.157229 obj = -14.495090, rho = -0.109410 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.135159 obj = -16.160290, rho = -0.147929 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 81 nu = 0.115912 obj = -18.199496, rho = -0.146293 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *...* optimization finished, #iter = 372 nu = 0.101813 obj = -20.670125, rho = -0.152313 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 164 nu = 0.090628 obj = -23.556861, rho = -0.092985 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 264 nu = 0.080944 obj = -26.847302, rho = -0.021713 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 146 nu = 0.074516 obj = -30.449861, rho = -0.047266 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 141 nu = 0.066513 obj = -34.096812, rho = -0.083953 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 190 nu = 0.059119 obj = -37.925268, rho = -0.128406 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 77 nu = 0.582314 obj = -3.978297, rho = -0.081645 nSV = 62, nBSV = 55 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 40 nu = 0.520288 obj = -4.545408, rho = -0.049949 nSV = 54, nBSV = 51 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 88 nu = 0.469623 obj = -5.148534, rho = -0.010382 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 58 nu = 0.412589 obj = -5.853820, rho = -0.017250 nSV = 47, nBSV = 39 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.367611 obj = -6.668318, rho = 0.002854 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 90 nu = 0.325296 obj = -7.627774, rho = -0.010670 nSV = 37, nBSV = 27 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 62 nu = 0.290212 obj = -8.775179, rho = -0.030325 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 97% (97/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 48 nu = 0.264362 obj = -10.104401, rho = 0.009379 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 27 nu = 0.246691 obj = -11.570312, rho = 0.074015 nSV = 26, nBSV = 23 Total nSV = 26 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 80 nu = 0.218537 obj = -13.085086, rho = 0.110576 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 97 nu = 0.193884 obj = -14.831468, rho = 0.157852 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 172 nu = 0.171694 obj = -16.878156, rho = 0.145189 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 242 nu = 0.149012 obj = -19.331784, rho = 0.142726 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.130874 obj = -22.399532, rho = 0.142740 nSV = 20, nBSV = 9 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 149 nu = 0.116741 obj = -26.243912, rho = 0.124830 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 131 nu = 0.106378 obj = -31.018434, rho = 0.130642 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 97% (97/100) (classification) Accuracy = 99.1% (991/1000) (classification) .* optimization finished, #iter = 142 nu = 0.102066 obj = -36.569005, rho = 0.199547 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 97% (97/100) (classification) Accuracy = 99% (990/1000) (classification) .* optimization finished, #iter = 169 nu = 0.095534 obj = -42.609007, rho = 0.249097 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 193 nu = 0.087313 obj = -49.498642, rho = 0.246785 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*.* optimization finished, #iter = 393 nu = 0.079533 obj = -57.398937, rho = 0.223263 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 33 nu = 0.544417 obj = -3.581708, rho = -0.123756 nSV = 56, nBSV = 53 Total nSV = 56 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 33 nu = 0.480868 obj = -4.009028, rho = -0.123727 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 30 nu = 0.424186 obj = -4.486026, rho = -0.093594 nSV = 44, nBSV = 41 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 31 nu = 0.373630 obj = -5.010080, rho = -0.087330 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 94 nu = 0.330213 obj = -5.572844, rho = -0.084068 nSV = 38, nBSV = 30 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 29 nu = 0.290126 obj = -6.205986, rho = -0.127770 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *..* optimization finished, #iter = 221 nu = 0.255881 obj = -6.848442, rho = -0.200548 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 194 nu = 0.220159 obj = -7.568479, rho = -0.206342 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.194000 obj = -8.335779, rho = -0.262174 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.164438 obj = -9.166754, rho = -0.265421 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.143554 obj = -10.113444, rho = -0.239291 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 50 nu = 0.126933 obj = -11.022320, rho = -0.355591 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 48 nu = 0.113076 obj = -11.930102, rho = -0.281367 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.097823 obj = -12.537630, rho = -0.373416 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.082669 obj = -13.027192, rho = -0.409096 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 198 nu = 0.068101 obj = -13.297441, rho = -0.411273 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..* optimization finished, #iter = 235 nu = 0.054330 obj = -13.479454, rho = -0.417343 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..* optimization finished, #iter = 255 nu = 0.044080 obj = -13.574680, rho = -0.437246 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..* optimization finished, #iter = 255 nu = 0.034592 obj = -13.574680, rho = -0.437246 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..* optimization finished, #iter = 255 nu = 0.027147 obj = -13.574680, rho = -0.437246 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 45 nu = 0.609829 obj = -4.294034, rho = -0.030697 nSV = 63, nBSV = 59 Total nSV = 63 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 37 nu = 0.560000 obj = -4.927149, rho = 0.008977 nSV = 57, nBSV = 55 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 48 nu = 0.511368 obj = -5.604497, rho = 0.021625 nSV = 55, nBSV = 48 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 40 nu = 0.465417 obj = -6.342152, rho = -0.009944 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 30 nu = 0.414182 obj = -7.123320, rho = -0.022071 nSV = 43, nBSV = 40 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.370182 obj = -7.936352, rho = 0.041166 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 90 nu = 0.319956 obj = -8.833342, rho = 0.048896 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 64 nu = 0.274558 obj = -9.905109, rho = 0.027848 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 56 nu = 0.240064 obj = -11.157938, rho = 0.064112 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 36 nu = 0.211761 obj = -12.629329, rho = 0.118099 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 69 nu = 0.190900 obj = -14.233667, rho = 0.081531 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 61 nu = 0.172561 obj = -15.926581, rho = -0.014490 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 77 nu = 0.149893 obj = -17.699382, rho = -0.066734 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 80 nu = 0.128194 obj = -19.838774, rho = -0.059365 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 85 nu = 0.111820 obj = -22.412678, rho = -0.077666 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 59 nu = 0.098840 obj = -25.479603, rho = -0.047111 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 183 nu = 0.089163 obj = -28.854339, rho = 0.042035 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 279 nu = 0.079989 obj = -32.592390, rho = 0.165605 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 143 nu = 0.069929 obj = -36.797698, rho = 0.235809 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.062482 obj = -41.587946, rho = 0.334720 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 36 nu = 0.584559 obj = -3.962280, rho = -0.187156 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 43 nu = 0.527173 obj = -4.478483, rho = -0.171692 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 58 nu = 0.463027 obj = -5.059689, rho = -0.174975 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 55 nu = 0.409832 obj = -5.731466, rho = -0.226579 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 50 nu = 0.362552 obj = -6.514134, rho = -0.272315 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 31 nu = 0.333174 obj = -7.390855, rho = -0.201704 nSV = 35, nBSV = 31 Total nSV = 35 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 91 nu = 0.301583 obj = -8.263517, rho = -0.191760 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 49 nu = 0.262428 obj = -9.213987, rho = -0.207587 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.229529 obj = -10.254214, rho = -0.244090 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.202728 obj = -11.420361, rho = -0.321003 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 84 nu = 0.181210 obj = -12.533011, rho = -0.423709 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.154514 obj = -13.742720, rho = -0.310224 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 95 nu = 0.130777 obj = -15.119366, rho = -0.299074 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 79 nu = 0.114009 obj = -16.771297, rho = -0.200107 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.099389 obj = -18.463043, rho = -0.141361 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 198 nu = 0.089006 obj = -20.100563, rho = -0.225706 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*.* optimization finished, #iter = 357 nu = 0.077977 obj = -21.545622, rho = -0.369722 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*......* optimization finished, #iter = 776 nu = 0.065052 obj = -22.965089, rho = -0.438045 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ...*.* optimization finished, #iter = 422 nu = 0.054600 obj = -24.434298, rho = -0.504842 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ..*.* optimization finished, #iter = 366 nu = 0.044753 obj = -26.052654, rho = -0.494243 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 48 nu = 0.627300 obj = -4.337410, rho = -0.202684 nSV = 65, nBSV = 59 Total nSV = 65 Accuracy = 95% (95/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 40 nu = 0.560520 obj = -4.973701, rho = -0.190798 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 95% (95/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 48 nu = 0.504462 obj = -5.692372, rho = -0.200661 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 95% (95/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 63 nu = 0.451813 obj = -6.506880, rho = -0.173055 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 95% (95/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 44 nu = 0.404018 obj = -7.457195, rho = -0.223864 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 96% (96/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 62 nu = 0.357924 obj = -8.583499, rho = -0.237088 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 96% (96/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 93 nu = 0.329793 obj = -9.916231, rho = -0.178148 nSV = 37, nBSV = 28 Total nSV = 37 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 64 nu = 0.292113 obj = -11.452221, rho = -0.161783 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 76 nu = 0.260735 obj = -13.323229, rho = -0.162815 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 58 nu = 0.237111 obj = -15.604538, rho = -0.184536 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 173 nu = 0.213260 obj = -18.341593, rho = -0.181593 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 97% (97/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.193558 obj = -21.766510, rho = -0.145554 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 154 nu = 0.182253 obj = -25.908411, rho = -0.070757 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.170146 obj = -30.747217, rho = 0.047141 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 55 nu = 0.158272 obj = -36.569856, rho = 0.149042 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 148 nu = 0.146375 obj = -43.505444, rho = 0.210253 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 96 nu = 0.138744 obj = -51.757829, rho = 0.331669 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 198 nu = 0.128226 obj = -61.392566, rho = 0.402353 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 139 nu = 0.116191 obj = -73.382629, rho = 0.461470 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) ..*.*.* optimization finished, #iter = 440 nu = 0.106590 obj = -88.589043, rho = 0.503708 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 45 nu = 0.545148 obj = -3.710507, rho = -0.174023 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 40 nu = 0.483065 obj = -4.218753, rho = -0.161207 nSV = 53, nBSV = 46 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 82 nu = 0.433765 obj = -4.803051, rho = -0.122683 nSV = 47, nBSV = 40 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.390340 obj = -5.459346, rho = -0.069466 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 40 nu = 0.343371 obj = -6.194965, rho = -0.049895 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 35 nu = 0.303602 obj = -7.081155, rho = -0.055405 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 25 nu = 0.284301 obj = -8.033756, rho = 0.004998 nSV = 30, nBSV = 26 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 26 nu = 0.253951 obj = -9.013506, rho = -0.079838 nSV = 28, nBSV = 24 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 55 nu = 0.230698 obj = -10.002387, rho = -0.039869 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 73 nu = 0.197820 obj = -11.012429, rho = -0.007044 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 72 nu = 0.171750 obj = -12.155384, rho = -0.028261 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 58 nu = 0.159089 obj = -13.285276, rho = -0.202975 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 171 nu = 0.133230 obj = -14.208255, rho = -0.239623 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.109891 obj = -15.293684, rho = -0.241635 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 201 nu = 0.093631 obj = -16.528354, rho = -0.203106 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 174 nu = 0.082600 obj = -17.681027, rho = -0.119286 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*..* optimization finished, #iter = 311 nu = 0.067789 obj = -18.720086, rho = -0.119973 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 161 nu = 0.058165 obj = -19.710866, rho = -0.071455 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 228 nu = 0.048051 obj = -20.528982, rho = -0.095513 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 154 nu = 0.039112 obj = -21.377180, rho = -0.087461 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 42 nu = 0.581866 obj = -3.878044, rho = -0.187373 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 52 nu = 0.521791 obj = -4.364053, rho = -0.274494 nSV = 55, nBSV = 48 Total nSV = 55 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.455632 obj = -4.908988, rho = -0.300537 nSV = 49, nBSV = 42 Total nSV = 49 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.397984 obj = -5.542669, rho = -0.307384 nSV = 45, nBSV = 35 Total nSV = 45 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 55 nu = 0.349097 obj = -6.311713, rho = -0.325598 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 96% (96/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 87 nu = 0.315488 obj = -7.176534, rho = -0.345493 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 72 nu = 0.279866 obj = -8.151968, rho = -0.355852 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 42 nu = 0.250247 obj = -9.272780, rho = -0.364264 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 41 nu = 0.220839 obj = -10.595021, rho = -0.357664 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.202278 obj = -11.971097, rho = -0.291549 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 79 nu = 0.178478 obj = -13.579374, rho = -0.310112 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 148 nu = 0.158638 obj = -15.346866, rho = -0.302136 nSV = 22, nBSV = 11 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 269 nu = 0.137769 obj = -17.477702, rho = -0.309275 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 85 nu = 0.123987 obj = -20.017864, rho = -0.345707 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 88 nu = 0.113234 obj = -22.782863, rho = -0.238345 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 90 nu = 0.103235 obj = -25.685586, rho = -0.196465 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*..* optimization finished, #iter = 301 nu = 0.088902 obj = -28.952108, rho = -0.237010 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 172 nu = 0.076857 obj = -33.012425, rho = -0.282868 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 167 nu = 0.069802 obj = -37.967883, rho = -0.322758 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 159 nu = 0.066648 obj = -42.945265, rho = -0.357476 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 48 nu = 0.598246 obj = -4.119443, rho = -0.091403 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 64 nu = 0.541314 obj = -4.690256, rho = -0.038038 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 59 nu = 0.490095 obj = -5.317893, rho = 0.022662 nSV = 52, nBSV = 45 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 80 nu = 0.427484 obj = -6.027231, rho = 0.022170 nSV = 47, nBSV = 40 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 41 nu = 0.380175 obj = -6.866901, rho = -0.000909 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 39 nu = 0.339538 obj = -7.836993, rho = -0.015727 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 58 nu = 0.302561 obj = -8.940258, rho = 0.000875 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 35 nu = 0.272242 obj = -10.221373, rho = 0.012757 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 55 nu = 0.244473 obj = -11.645305, rho = 0.024627 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 71 nu = 0.218303 obj = -13.276406, rho = 0.058887 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) *.* optimization finished, #iter = 148 nu = 0.192344 obj = -15.193427, rho = 0.057970 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 87 nu = 0.171809 obj = -17.499116, rho = 0.048445 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 53 nu = 0.157371 obj = -20.143261, rho = -0.025945 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 51 nu = 0.146763 obj = -22.991746, rho = -0.107127 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 83 nu = 0.138989 obj = -25.665982, rho = -0.124989 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) ....*....* optimization finished, #iter = 804 nu = 0.120328 obj = -28.015702, rho = -0.105010 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) ..*..* optimization finished, #iter = 409 nu = 0.103516 obj = -30.663828, rho = -0.109945 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 193 nu = 0.089750 obj = -33.339348, rho = -0.095653 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) .......*..........* optimization finished, #iter = 1785 nu = 0.075623 obj = -36.317806, rho = -0.095183 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) ..*....* optimization finished, #iter = 649 nu = 0.066233 obj = -39.476870, rho = -0.146733 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 60 nu = 0.538306 obj = -3.636512, rho = -0.244944 nSV = 57, nBSV = 50 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 46 nu = 0.482419 obj = -4.114539, rho = -0.336104 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 56 nu = 0.430496 obj = -4.638787, rho = -0.335014 nSV = 46, nBSV = 40 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.378387 obj = -5.228727, rho = -0.333229 nSV = 42, nBSV = 33 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 45 nu = 0.333349 obj = -5.931861, rho = -0.304091 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 40 nu = 0.296133 obj = -6.739833, rho = -0.341420 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 41 nu = 0.259254 obj = -7.684758, rho = -0.385165 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.233968 obj = -8.786615, rho = -0.340158 nSV = 26, nBSV = 21 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 41 nu = 0.206886 obj = -10.034670, rho = -0.328258 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 36 nu = 0.190744 obj = -11.429756, rho = -0.518843 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 50 nu = 0.170310 obj = -12.946680, rho = -0.609442 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 41 nu = 0.152761 obj = -14.647838, rho = -0.574247 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.134619 obj = -16.513021, rho = -0.582611 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 90 nu = 0.124936 obj = -18.505249, rho = -0.424829 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.111334 obj = -20.348101, rho = -0.531719 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 194 nu = 0.094254 obj = -22.315593, rho = -0.571175 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.080956 obj = -24.635596, rho = -0.618127 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 68 nu = 0.073310 obj = -27.099837, rho = -0.646249 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.067008 obj = -28.708663, rho = -0.560284 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 158 nu = 0.058445 obj = -29.423182, rho = -0.472481 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 40 nu = 0.529864 obj = -3.624527, rho = 0.063449 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 77 nu = 0.473469 obj = -4.122321, rho = 0.002257 nSV = 52, nBSV = 44 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 45 nu = 0.420001 obj = -4.708969, rho = -0.024413 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 33 nu = 0.382175 obj = -5.365570, rho = 0.040075 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 51 nu = 0.340618 obj = -6.067842, rho = -0.000073 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 42 nu = 0.301394 obj = -6.876308, rho = 0.022880 nSV = 34, nBSV = 26 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 46 nu = 0.264048 obj = -7.846204, rho = 0.048586 nSV = 28, nBSV = 24 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 73 nu = 0.241145 obj = -8.939261, rho = 0.019295 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 87 nu = 0.211208 obj = -10.200333, rho = 0.035058 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.187453 obj = -11.709187, rho = 0.102677 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 44 nu = 0.172693 obj = -13.453049, rho = 0.199996 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 94 nu = 0.158292 obj = -15.349762, rho = 0.147328 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 39 nu = 0.142827 obj = -17.341971, rho = -0.052747 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 78 nu = 0.129737 obj = -19.434387, rho = -0.106866 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 158 nu = 0.116785 obj = -21.412637, rho = -0.093838 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..* optimization finished, #iter = 265 nu = 0.101315 obj = -23.413550, rho = -0.066397 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..* optimization finished, #iter = 299 nu = 0.087092 obj = -25.602251, rho = -0.122643 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 194 nu = 0.073226 obj = -28.052103, rho = -0.141577 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 184 nu = 0.063093 obj = -30.864281, rho = -0.177116 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 133 nu = 0.055968 obj = -33.816331, rho = -0.206456 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 50 nu = 0.590374 obj = -4.083529, rho = -0.226454 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 41 nu = 0.525623 obj = -4.675181, rho = -0.239097 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 39 nu = 0.472355 obj = -5.354473, rho = -0.264082 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 51 nu = 0.422399 obj = -6.150738, rho = -0.249480 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.373195 obj = -7.099123, rho = -0.265267 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 64 nu = 0.336117 obj = -8.244041, rho = -0.222763 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 60 nu = 0.316766 obj = -9.523947, rho = -0.229518 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.281140 obj = -10.981753, rho = -0.245371 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.262053 obj = -12.699839, rho = -0.292436 nSV = 29, nBSV = 25 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 67 nu = 0.244820 obj = -14.464265, rho = -0.347035 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 73 nu = 0.230512 obj = -16.106391, rho = -0.395609 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.200704 obj = -17.605045, rho = -0.396217 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..* optimization finished, #iter = 261 nu = 0.173630 obj = -19.097661, rho = -0.431014 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 231 nu = 0.145391 obj = -20.779873, rho = -0.430585 nSV = 21, nBSV = 10 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.124399 obj = -22.729046, rho = -0.484071 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 136 nu = 0.108908 obj = -24.798053, rho = -0.618913 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 177 nu = 0.094702 obj = -26.826424, rho = -0.707511 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *....* optimization finished, #iter = 476 nu = 0.082975 obj = -28.494745, rho = -0.869139 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 166 nu = 0.067973 obj = -30.028902, rho = -0.936923 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.056611 obj = -31.745131, rho = -1.030679 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 43 nu = 0.593704 obj = -3.963154, rho = -0.263130 nSV = 62, nBSV = 57 Total nSV = 62 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 39 nu = 0.529414 obj = -4.474798, rho = -0.286333 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 85 nu = 0.465630 obj = -5.037232, rho = -0.307752 nSV = 51, nBSV = 43 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 37 nu = 0.412908 obj = -5.690685, rho = -0.375798 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 36 nu = 0.374710 obj = -6.392222, rho = -0.416245 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 39 nu = 0.326144 obj = -7.134875, rho = -0.425241 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 40 nu = 0.284944 obj = -7.992537, rho = -0.476537 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.247179 obj = -8.979633, rho = -0.493909 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 56 nu = 0.216043 obj = -10.172626, rho = -0.516535 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 40 nu = 0.195963 obj = -11.527713, rho = -0.616273 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 53 nu = 0.177428 obj = -12.926776, rho = -0.531795 nSV = 20, nBSV = 15 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 65 nu = 0.155318 obj = -14.434021, rho = -0.573563 nSV = 18, nBSV = 13 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 175 nu = 0.140841 obj = -15.938584, rho = -0.679404 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) ..* optimization finished, #iter = 280 nu = 0.119281 obj = -17.532927, rho = -0.639735 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.101080 obj = -19.474871, rho = -0.583207 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 177 nu = 0.086718 obj = -21.850894, rho = -0.516048 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*..* optimization finished, #iter = 335 nu = 0.075615 obj = -24.735314, rho = -0.487048 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 178 nu = 0.066486 obj = -28.163062, rho = -0.413463 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*.....*.* optimization finished, #iter = 779 nu = 0.059039 obj = -32.234087, rho = -0.372649 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.054902 obj = -36.815670, rho = -0.224697 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 35 nu = 0.609864 obj = -4.318800, rho = -0.083745 nSV = 62, nBSV = 60 Total nSV = 62 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 78 nu = 0.562411 obj = -4.942008, rho = -0.022921 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 39 nu = 0.507665 obj = -5.642323, rho = 0.028410 nSV = 53, nBSV = 48 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 51 nu = 0.468729 obj = -6.354662, rho = -0.072094 nSV = 51, nBSV = 43 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 43 nu = 0.411632 obj = -7.118279, rho = -0.095568 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 56 nu = 0.362140 obj = -7.968371, rho = -0.014803 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 66 nu = 0.324946 obj = -8.910688, rho = -0.044193 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.287144 obj = -9.858997, rho = -0.074016 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.251544 obj = -10.855187, rho = -0.086837 nSV = 31, nBSV = 21 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.....* optimization finished, #iter = 506 nu = 0.215857 obj = -11.926725, rho = -0.142114 nSV = 27, nBSV = 17 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 94 nu = 0.189304 obj = -13.133562, rho = -0.206810 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.164009 obj = -14.283321, rho = -0.251807 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.140531 obj = -15.519963, rho = -0.344755 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *..* optimization finished, #iter = 204 nu = 0.117020 obj = -16.940450, rho = -0.322015 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 155 nu = 0.102347 obj = -18.535626, rho = -0.256775 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.089176 obj = -20.052341, rho = -0.219090 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 75 nu = 0.079206 obj = -21.413093, rho = -0.445403 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 166 nu = 0.066614 obj = -22.414527, rho = -0.542446 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 180 nu = 0.055204 obj = -23.312581, rho = -0.465554 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 193 nu = 0.047261 obj = -23.633053, rho = -0.311977 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 54 nu = 0.553202 obj = -3.596412, rho = -0.124746 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 44 nu = 0.486581 obj = -4.025949, rho = -0.137334 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 61 nu = 0.427936 obj = -4.495036, rho = -0.170097 nSV = 46, nBSV = 38 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 54 nu = 0.375395 obj = -5.025257, rho = -0.097871 nSV = 41, nBSV = 33 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 55 nu = 0.323849 obj = -5.630729, rho = -0.097610 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 35 nu = 0.286678 obj = -6.315907, rho = -0.112037 nSV = 32, nBSV = 26 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.253257 obj = -7.085551, rho = -0.182883 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 53 nu = 0.222056 obj = -7.957922, rho = -0.238279 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.195764 obj = -8.917275, rho = -0.216441 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.174238 obj = -9.987413, rho = -0.228621 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *..* optimization finished, #iter = 285 nu = 0.153357 obj = -11.161791, rho = -0.281641 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.134328 obj = -12.402089, rho = -0.328788 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 91 nu = 0.119410 obj = -13.773017, rho = -0.418312 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 199 nu = 0.105154 obj = -15.104967, rho = -0.538399 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 81 nu = 0.090056 obj = -16.575823, rho = -0.663903 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 190 nu = 0.076574 obj = -18.186136, rho = -0.742831 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 63 nu = 0.068848 obj = -19.952363, rho = -0.840278 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.060328 obj = -21.320423, rho = -0.911791 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 67 nu = 0.050491 obj = -22.663097, rho = -0.913766 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.043253 obj = -23.943193, rho = -0.854197 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 43 nu = 0.530522 obj = -3.656881, rho = -0.093764 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 96% (96/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 51 nu = 0.471041 obj = -4.181033, rho = -0.087791 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 96% (96/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 45 nu = 0.422490 obj = -4.796541, rho = -0.057771 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 96% (96/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 36 nu = 0.378998 obj = -5.514857, rho = -0.121239 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 96% (96/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 46 nu = 0.343259 obj = -6.338661, rho = -0.176999 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 96% (96/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 42 nu = 0.312547 obj = -7.278643, rho = -0.214669 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 48 nu = 0.281340 obj = -8.308238, rho = -0.213498 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 51 nu = 0.258326 obj = -9.451077, rho = -0.220279 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.232487 obj = -10.670308, rho = -0.281925 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 136 nu = 0.204835 obj = -12.016006, rho = -0.221104 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.178541 obj = -13.569690, rho = -0.230867 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 67 nu = 0.155658 obj = -15.456639, rho = -0.242205 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.140948 obj = -17.614851, rho = -0.278832 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 98 nu = 0.126069 obj = -20.023285, rho = -0.324002 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) *....* optimization finished, #iter = 402 nu = 0.113314 obj = -22.742035, rho = -0.409533 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) ..*.* optimization finished, #iter = 316 nu = 0.098475 obj = -25.881256, rho = -0.416147 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) ..*...*..............* optimization finished, #iter = 1930 nu = 0.087069 obj = -29.666386, rho = -0.469432 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) ..* optimization finished, #iter = 285 nu = 0.077593 obj = -34.309109, rho = -0.434247 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) ..*.* optimization finished, #iter = 391 nu = 0.069515 obj = -39.812000, rho = -0.381758 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 216 nu = 0.064543 obj = -46.259435, rho = -0.438722 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 36 nu = 0.544852 obj = -3.740411, rho = -0.186291 nSV = 56, nBSV = 52 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 37 nu = 0.499545 obj = -4.252427, rho = -0.186454 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 33 nu = 0.439788 obj = -4.816283, rho = -0.232515 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 30 nu = 0.408869 obj = -5.411045, rho = -0.277706 nSV = 43, nBSV = 40 Total nSV = 43 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 30 nu = 0.358753 obj = -5.989409, rho = -0.268610 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 67 nu = 0.316477 obj = -6.597373, rho = -0.290210 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 89 nu = 0.273010 obj = -7.254869, rho = -0.283067 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.232467 obj = -7.988392, rho = -0.302001 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.198157 obj = -8.861337, rho = -0.297680 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 75 nu = 0.171337 obj = -9.922691, rho = -0.305714 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 89 nu = 0.151005 obj = -11.111530, rho = -0.294324 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 29 nu = 0.140000 obj = -12.340492, rho = -0.258274 nSV = 16, nBSV = 11 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.126249 obj = -13.211713, rho = -0.264715 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*.* optimization finished, #iter = 375 nu = 0.105435 obj = -14.039474, rho = -0.340642 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*.......* optimization finished, #iter = 966 nu = 0.088607 obj = -14.814718, rho = -0.383731 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ...*.....* optimization finished, #iter = 844 nu = 0.071909 obj = -15.707855, rho = -0.382742 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.......* optimization finished, #iter = 855 nu = 0.059550 obj = -16.810084, rho = -0.358548 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 171 nu = 0.050018 obj = -18.075202, rho = -0.321135 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 87 nu = 0.043859 obj = -19.291820, rho = -0.195002 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 148 nu = 0.039337 obj = -19.840512, rho = -0.041205 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 41 nu = 0.601077 obj = -4.000741, rho = -0.108547 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.528147 obj = -4.521757, rho = -0.119422 nSV = 54, nBSV = 50 Total nSV = 54 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 33 nu = 0.466871 obj = -5.133172, rho = -0.104975 nSV = 49, nBSV = 45 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 46 nu = 0.432125 obj = -5.782642, rho = -0.085681 nSV = 45, nBSV = 38 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 48 nu = 0.375932 obj = -6.463062, rho = -0.038418 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 31 nu = 0.331408 obj = -7.226327, rho = -0.045626 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 44 nu = 0.292566 obj = -8.051616, rho = -0.009333 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 40 nu = 0.255289 obj = -8.977707, rho = 0.000184 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 34 nu = 0.222623 obj = -10.035029, rho = -0.031568 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 98 nu = 0.199032 obj = -11.141986, rho = -0.056477 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.173526 obj = -12.304089, rho = -0.026274 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 60 nu = 0.150326 obj = -13.573106, rho = -0.013403 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 88 nu = 0.134217 obj = -14.904489, rho = 0.014557 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 88 nu = 0.117513 obj = -16.129439, rho = 0.014217 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 178 nu = 0.100071 obj = -17.276035, rho = 0.024367 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.084035 obj = -18.484876, rho = 0.058190 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) ..*.* optimization finished, #iter = 345 nu = 0.070351 obj = -19.741646, rho = 0.074931 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) ..*.* optimization finished, #iter = 336 nu = 0.057779 obj = -21.243999, rho = 0.068837 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 271 nu = 0.048604 obj = -22.969524, rho = 0.023944 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 98 nu = 0.041355 obj = -25.024099, rho = 0.001039 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 37 nu = 0.567250 obj = -3.798798, rho = -0.092721 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 29 nu = 0.500000 obj = -4.301608, rho = -0.122757 nSV = 51, nBSV = 49 Total nSV = 51 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 53 nu = 0.457555 obj = -4.825882, rho = -0.134934 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 46 nu = 0.397735 obj = -5.421545, rho = -0.117319 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 42 nu = 0.353085 obj = -6.076533, rho = -0.130713 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 48 nu = 0.312377 obj = -6.808458, rho = -0.151133 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 37 nu = 0.272306 obj = -7.603769, rho = -0.168308 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 73 nu = 0.240438 obj = -8.493741, rho = -0.213904 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 78 nu = 0.209539 obj = -9.494762, rho = -0.201427 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.183681 obj = -10.646043, rho = -0.235631 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 96 nu = 0.160161 obj = -11.965255, rho = -0.303422 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 162 nu = 0.140482 obj = -13.492426, rho = -0.377883 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.124755 obj = -15.242797, rho = -0.378915 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.111232 obj = -17.184897, rho = -0.364329 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 138 nu = 0.103591 obj = -19.169885, rho = -0.238450 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.093207 obj = -20.866399, rho = -0.080525 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 156 nu = 0.078605 obj = -22.521172, rho = -0.042190 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 218 nu = 0.066879 obj = -24.180942, rho = -0.031995 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 161 nu = 0.055842 obj = -26.067098, rho = -0.086852 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*.* optimization finished, #iter = 321 nu = 0.046772 obj = -28.222607, rho = -0.123890 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.579421 obj = -4.023414, rho = 0.094620 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 78 nu = 0.521707 obj = -4.594541, rho = 0.123212 nSV = 57, nBSV = 49 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 71 nu = 0.455724 obj = -5.282762, rho = 0.129911 nSV = 50, nBSV = 43 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 52 nu = 0.412719 obj = -6.105005, rho = 0.115052 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 51 nu = 0.369003 obj = -7.080938, rho = 0.143400 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 47 nu = 0.339756 obj = -8.260196, rho = 0.161204 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 38 nu = 0.305082 obj = -9.611370, rho = 0.168888 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 42 nu = 0.281534 obj = -11.225984, rho = 0.042723 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 59 nu = 0.259288 obj = -13.087219, rho = -0.052374 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 37 nu = 0.238084 obj = -15.263000, rho = -0.115103 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 176 nu = 0.217622 obj = -17.747428, rho = -0.128016 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.199747 obj = -20.611255, rho = -0.096698 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.184408 obj = -23.801614, rho = -0.036938 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 67 nu = 0.165709 obj = -27.412890, rho = -0.041543 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 83 nu = 0.148235 obj = -31.587874, rho = -0.069498 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.134013 obj = -36.533232, rho = -0.113952 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.125462 obj = -42.174698, rho = -0.108702 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.118589 obj = -47.767351, rho = 0.008099 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 91 nu = 0.108226 obj = -52.945574, rho = 0.080055 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 215 nu = 0.094013 obj = -58.291451, rho = 0.104674 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 34 nu = 0.540760 obj = -3.691913, rho = -0.061358 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 44 nu = 0.488129 obj = -4.190044, rho = -0.150260 nSV = 51, nBSV = 44 Total nSV = 51 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 44 nu = 0.431995 obj = -4.756449, rho = -0.196464 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 34 nu = 0.388202 obj = -5.390856, rho = -0.245415 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 39 nu = 0.353791 obj = -6.063667, rho = -0.195193 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 45 nu = 0.314534 obj = -6.749390, rho = -0.133616 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.273482 obj = -7.476708, rho = -0.156995 nSV = 32, nBSV = 23 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *..* optimization finished, #iter = 215 nu = 0.234335 obj = -8.322512, rho = -0.162882 nSV = 31, nBSV = 19 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 71 nu = 0.205533 obj = -9.322706, rho = -0.185876 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 151 nu = 0.178139 obj = -10.477413, rho = -0.200851 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 79 nu = 0.155235 obj = -11.868263, rho = -0.239922 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.139678 obj = -13.417786, rho = -0.303556 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 185 nu = 0.122904 obj = -15.156052, rho = -0.350275 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 153 nu = 0.109219 obj = -17.212583, rho = -0.428604 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.101634 obj = -19.306764, rho = -0.536515 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 253 nu = 0.088568 obj = -21.380119, rho = -0.666677 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 158 nu = 0.075677 obj = -23.821753, rho = -0.741145 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 82 nu = 0.069142 obj = -26.488319, rho = -0.961010 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 67 nu = 0.062508 obj = -28.731724, rho = -1.211172 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.056733 obj = -30.273443, rho = -1.187267 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 43 nu = 0.603163 obj = -4.170569, rho = -0.144749 nSV = 62, nBSV = 59 Total nSV = 62 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 47 nu = 0.543184 obj = -4.761783, rho = -0.139088 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 78 nu = 0.488620 obj = -5.440695, rho = -0.164724 nSV = 53, nBSV = 47 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 83 nu = 0.440119 obj = -6.186763, rho = -0.231763 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 64 nu = 0.391377 obj = -7.030800, rho = -0.224469 nSV = 42, nBSV = 34 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 44 nu = 0.355770 obj = -7.987017, rho = -0.222967 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 87 nu = 0.316920 obj = -8.980281, rho = -0.266705 nSV = 36, nBSV = 28 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 75 nu = 0.285042 obj = -10.074795, rho = -0.302855 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 81 nu = 0.252364 obj = -11.195457, rho = -0.236291 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.217686 obj = -12.433339, rho = -0.213944 nSV = 28, nBSV = 18 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.187954 obj = -13.903010, rho = -0.193878 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 49 nu = 0.171611 obj = -15.520963, rho = -0.374082 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 64 nu = 0.155593 obj = -16.960815, rho = -0.500214 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 250 nu = 0.132156 obj = -18.217289, rho = -0.534146 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *..* optimization finished, #iter = 204 nu = 0.111913 obj = -19.585247, rho = -0.552262 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 161 nu = 0.093748 obj = -21.041013, rho = -0.577422 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.078764 obj = -22.732063, rho = -0.594327 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 126 nu = 0.066883 obj = -24.648753, rho = -0.630608 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 143 nu = 0.056886 obj = -26.709132, rho = -0.672234 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 148 nu = 0.050716 obj = -28.667876, rho = -0.751739 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 36 nu = 0.518683 obj = -3.387769, rho = 0.049737 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 51 nu = 0.457480 obj = -3.784917, rho = 0.068559 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 51 nu = 0.403118 obj = -4.224695, rho = 0.103632 nSV = 42, nBSV = 38 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 41 nu = 0.361458 obj = -4.695720, rho = 0.074933 nSV = 39, nBSV = 35 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 54 nu = 0.312615 obj = -5.174859, rho = 0.101819 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.270724 obj = -5.716997, rho = 0.100813 nSV = 29, nBSV = 24 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.231696 obj = -6.315299, rho = 0.116452 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 40 nu = 0.201811 obj = -7.030833, rho = 0.152533 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 59 nu = 0.173709 obj = -7.830788, rho = 0.183554 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 25 nu = 0.150817 obj = -8.780847, rho = 0.165769 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 25 nu = 0.137690 obj = -9.791362, rho = 0.112713 nSV = 16, nBSV = 12 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 26 nu = 0.126855 obj = -10.653057, rho = 0.126974 nSV = 14, nBSV = 9 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 52 nu = 0.106192 obj = -11.382102, rho = 0.117067 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 295 nu = 0.089390 obj = -12.179339, rho = 0.095936 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ...* optimization finished, #iter = 360 nu = 0.074068 obj = -13.099075, rho = 0.100468 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*..* optimization finished, #iter = 429 nu = 0.061143 obj = -14.234670, rho = 0.100327 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .**.* optimization finished, #iter = 164 nu = 0.051129 obj = -15.679741, rho = 0.099210 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 49 nu = 0.045977 obj = -17.340183, rho = 0.173600 nSV = 8, nBSV = 3 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 51 nu = 0.044217 obj = -18.448986, rho = 0.326389 nSV = 9, nBSV = 3 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.036986 obj = -18.697309, rho = 0.406588 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 43 nu = 0.575668 obj = -3.952478, rho = -0.204393 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 34 nu = 0.521514 obj = -4.502305, rho = -0.169772 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 32 nu = 0.463226 obj = -5.096210, rho = -0.154517 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 40 nu = 0.416419 obj = -5.771536, rho = -0.180212 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 40 nu = 0.373312 obj = -6.524635, rho = -0.133122 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 40 nu = 0.331948 obj = -7.321886, rho = -0.125051 nSV = 36, nBSV = 30 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.296800 obj = -8.178928, rho = -0.070760 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 186 nu = 0.258884 obj = -9.114920, rho = -0.126439 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*...*.....* optimization finished, #iter = 898 nu = 0.222159 obj = -10.194059, rho = -0.110978 nSV = 29, nBSV = 18 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *...*.* optimization finished, #iter = 374 nu = 0.195321 obj = -11.483711, rho = -0.091663 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 176 nu = 0.170767 obj = -12.952519, rho = -0.118537 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.150463 obj = -14.687626, rho = -0.141699 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 80 nu = 0.132538 obj = -16.731996, rho = -0.172947 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 63 nu = 0.120833 obj = -19.070710, rho = -0.146403 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 98 nu = 0.112533 obj = -21.364723, rho = -0.143577 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 161 nu = 0.099621 obj = -23.554963, rho = -0.042871 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.086064 obj = -25.974899, rho = -0.081792 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 85 nu = 0.077307 obj = -28.403064, rho = -0.104084 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 96 nu = 0.067739 obj = -30.396944, rho = -0.193713 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.057233 obj = -32.217136, rho = -0.355287 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 48 nu = 0.558180 obj = -3.813520, rho = -0.054716 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 42 nu = 0.494325 obj = -4.351059, rho = -0.034335 nSV = 51, nBSV = 48 Total nSV = 51 Accuracy = 96% (96/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 63 nu = 0.445781 obj = -4.964665, rho = -0.080515 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 96% (96/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 51 nu = 0.398219 obj = -5.649736, rho = -0.126862 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 38 nu = 0.355289 obj = -6.438740, rho = -0.165719 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 59 nu = 0.326630 obj = -7.299792, rho = -0.298369 nSV = 35, nBSV = 28 Total nSV = 35 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 29 nu = 0.288029 obj = -8.237460, rho = -0.256060 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 58 nu = 0.258729 obj = -9.255967, rho = -0.272886 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.225249 obj = -10.405842, rho = -0.242501 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 90 nu = 0.196892 obj = -11.777540, rho = -0.271742 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 67 nu = 0.171749 obj = -13.408748, rho = -0.286321 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 84 nu = 0.151457 obj = -15.421427, rho = -0.278713 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*...* optimization finished, #iter = 435 nu = 0.139257 obj = -17.727356, rho = -0.232284 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.125776 obj = -20.324668, rho = -0.263405 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 156 nu = 0.114021 obj = -23.206461, rho = -0.350831 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 217 nu = 0.104805 obj = -26.255513, rho = -0.467314 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*..* optimization finished, #iter = 384 nu = 0.091368 obj = -29.515737, rho = -0.500065 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 73 nu = 0.082403 obj = -33.371037, rho = -0.586352 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.071678 obj = -37.481848, rho = -0.599467 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.065203 obj = -42.278579, rho = -0.578219 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 64 nu = 0.543227 obj = -3.599480, rho = -0.146364 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 98 nu = 0.483673 obj = -4.039318, rho = -0.126195 nSV = 53, nBSV = 44 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 81 nu = 0.419493 obj = -4.540711, rho = -0.126106 nSV = 47, nBSV = 38 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 40 nu = 0.373421 obj = -5.132689, rho = -0.174418 nSV = 40, nBSV = 35 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.329576 obj = -5.772424, rho = -0.121161 nSV = 36, nBSV = 31 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 51 nu = 0.290159 obj = -6.503025, rho = -0.061159 nSV = 32, nBSV = 27 Total nSV = 32 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 41 nu = 0.261074 obj = -7.322240, rho = -0.018257 nSV = 28, nBSV = 24 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.229549 obj = -8.171256, rho = -0.060327 nSV = 27, nBSV = 17 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 57 nu = 0.197191 obj = -9.199059, rho = -0.035912 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 65 nu = 0.177350 obj = -10.396515, rho = 0.037069 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 68 nu = 0.155609 obj = -11.745880, rho = 0.055007 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 81 nu = 0.135433 obj = -13.310804, rho = 0.062032 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 76 nu = 0.118620 obj = -15.227812, rho = 0.071526 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 84 nu = 0.105631 obj = -17.544281, rho = 0.061387 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 53 nu = 0.096360 obj = -20.269200, rho = 0.058030 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 73 nu = 0.089260 obj = -23.228343, rho = -0.018728 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 252 nu = 0.082312 obj = -26.303853, rho = -0.153515 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.072496 obj = -29.686619, rho = -0.163206 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.063090 obj = -33.669955, rho = -0.199111 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 99 nu = 0.056530 obj = -38.259374, rho = -0.317451 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 61 nu = 0.611082 obj = -4.249933, rho = -0.171296 nSV = 63, nBSV = 56 Total nSV = 63 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 53 nu = 0.548469 obj = -4.880427, rho = -0.150118 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 47 nu = 0.493341 obj = -5.606780, rho = -0.151666 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 60 nu = 0.441624 obj = -6.447772, rho = -0.129324 nSV = 48, nBSV = 42 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 50 nu = 0.401233 obj = -7.413700, rho = -0.154884 nSV = 43, nBSV = 37 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 76 nu = 0.359932 obj = -8.526166, rho = -0.102240 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 96 nu = 0.331726 obj = -9.755079, rho = 0.001162 nSV = 38, nBSV = 29 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.291302 obj = -11.162066, rho = 0.002863 nSV = 35, nBSV = 25 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 61 nu = 0.265215 obj = -12.868544, rho = 0.052226 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 97 nu = 0.246836 obj = -14.689340, rho = 0.111411 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.227033 obj = -16.452079, rho = 0.229321 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 229 nu = 0.203158 obj = -18.284377, rho = 0.316064 nSV = 25, nBSV = 15 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*..* optimization finished, #iter = 488 nu = 0.175236 obj = -20.207986, rho = 0.401343 nSV = 21, nBSV = 10 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..**.* optimization finished, #iter = 265 nu = 0.153338 obj = -22.332401, rho = 0.483649 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) ..* optimization finished, #iter = 256 nu = 0.131207 obj = -24.600950, rho = 0.564067 nSV = 19, nBSV = 8 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*....* optimization finished, #iter = 569 nu = 0.113757 obj = -27.258698, rho = 0.580044 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) ...*.* optimization finished, #iter = 409 nu = 0.102490 obj = -30.007539, rho = 0.639318 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..*..* optimization finished, #iter = 465 nu = 0.090807 obj = -32.496943, rho = 0.890452 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) ....*..* optimization finished, #iter = 697 nu = 0.075780 obj = -34.825609, rho = 0.989042 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ....*.............* optimization finished, #iter = 1785 nu = 0.063202 obj = -37.457715, rho = 1.059217 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 45 nu = 0.562844 obj = -3.813324, rho = -0.086238 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 33 nu = 0.508149 obj = -4.324602, rho = -0.080923 nSV = 53, nBSV = 50 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 39 nu = 0.460865 obj = -4.857754, rho = -0.121926 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 32 nu = 0.410288 obj = -5.419713, rho = -0.151952 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 52 nu = 0.358621 obj = -6.014176, rho = -0.143213 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 49 nu = 0.312454 obj = -6.664491, rho = -0.113756 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.273400 obj = -7.393945, rho = -0.070372 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 41 nu = 0.238885 obj = -8.178897, rho = -0.090729 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 66 nu = 0.206762 obj = -9.001614, rho = -0.055602 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 82 nu = 0.177255 obj = -9.949803, rho = -0.029719 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.150230 obj = -11.068099, rho = -0.013550 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 250 nu = 0.132357 obj = -12.413572, rho = 0.048776 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 79 nu = 0.116069 obj = -13.866902, rho = 0.056827 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 196 nu = 0.104111 obj = -15.451428, rho = -0.052410 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 80 nu = 0.095214 obj = -16.961469, rho = -0.189951 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) ..*.* optimization finished, #iter = 307 nu = 0.082395 obj = -18.210924, rho = -0.312191 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 179 nu = 0.069323 obj = -19.553638, rho = -0.472705 nSV = 11, nBSV = 5 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 177 nu = 0.059334 obj = -20.869695, rho = -0.491522 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 43 nu = 0.051365 obj = -22.006795, rho = -0.590947 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 45 nu = 0.044390 obj = -22.474842, rho = -0.593909 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.562710 obj = -3.718291, rho = -0.124099 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 40 nu = 0.507709 obj = -4.166418, rho = -0.113748 nSV = 53, nBSV = 49 Total nSV = 53 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 28 nu = 0.452409 obj = -4.621230, rho = -0.095934 nSV = 46, nBSV = 43 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 58 nu = 0.393953 obj = -5.093697, rho = -0.094853 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 99.3% (993/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.336427 obj = -5.615955, rho = -0.080201 nSV = 39, nBSV = 30 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 99.4% (994/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.291738 obj = -6.212090, rho = -0.098532 nSV = 34, nBSV = 25 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 99.4% (994/1000) (classification) * optimization finished, #iter = 47 nu = 0.250560 obj = -6.916551, rho = -0.113729 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 99.4% (994/1000) (classification) * optimization finished, #iter = 92 nu = 0.223673 obj = -7.658932, rho = -0.072002 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 99.4% (994/1000) (classification) * optimization finished, #iter = 53 nu = 0.193498 obj = -8.443621, rho = -0.081657 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 99.3% (993/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.170154 obj = -9.267496, rho = -0.067058 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 38 nu = 0.150387 obj = -10.133199, rho = 0.030887 nSV = 18, nBSV = 13 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.128243 obj = -10.909559, rho = 0.062708 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 122 nu = 0.107635 obj = -11.798341, rho = 0.066571 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .**..* optimization finished, #iter = 377 nu = 0.089594 obj = -12.826899, rho = 0.059221 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 96 nu = 0.077316 obj = -14.028877, rho = 0.028451 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 189 nu = 0.066315 obj = -15.298737, rho = 0.041898 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.057297 obj = -16.649178, rho = 0.072075 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 186 nu = 0.051001 obj = -17.933435, rho = 0.055028 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 156 nu = 0.044605 obj = -18.724783, rho = 0.010174 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.038075 obj = -19.037505, rho = 0.007410 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 36 nu = 0.548675 obj = -3.686260, rho = -0.183439 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 42 nu = 0.488377 obj = -4.169143, rho = -0.234426 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 37 nu = 0.429781 obj = -4.726240, rho = -0.231068 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 34 nu = 0.382646 obj = -5.366006, rho = -0.198094 nSV = 41, nBSV = 37 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 39 nu = 0.343552 obj = -6.072722, rho = -0.160780 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 32 nu = 0.313374 obj = -6.844574, rho = -0.251304 nSV = 32, nBSV = 28 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 34 nu = 0.277071 obj = -7.646571, rho = -0.327509 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.247318 obj = -8.490357, rho = -0.346767 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 67 nu = 0.217656 obj = -9.351700, rho = -0.304761 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 73 nu = 0.186921 obj = -10.276485, rho = -0.286085 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 65 nu = 0.159498 obj = -11.296840, rho = -0.275977 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 58 nu = 0.142327 obj = -12.431414, rho = -0.324375 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 72 nu = 0.124627 obj = -13.419900, rho = -0.390829 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.105026 obj = -14.410511, rho = -0.401041 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 167 nu = 0.091473 obj = -15.241756, rho = -0.363645 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 136 nu = 0.076318 obj = -16.084544, rho = -0.350454 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*...* optimization finished, #iter = 460 nu = 0.063934 obj = -16.724943, rho = -0.359069 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 152 nu = 0.052751 obj = -17.316015, rho = -0.381223 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.044727 obj = -17.547218, rho = -0.395040 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.035100 obj = -17.547218, rho = -0.395040 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.573060 obj = -3.899000, rho = -0.322036 nSV = 59, nBSV = 54 Total nSV = 59 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 39 nu = 0.513509 obj = -4.425094, rho = -0.319377 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.461498 obj = -5.007497, rho = -0.306910 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 43 nu = 0.407513 obj = -5.655525, rho = -0.343643 nSV = 45, nBSV = 37 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 39 nu = 0.359504 obj = -6.400151, rho = -0.378026 nSV = 39, nBSV = 30 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 64 nu = 0.320300 obj = -7.268399, rho = -0.476451 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.285027 obj = -8.225599, rho = -0.527713 nSV = 33, nBSV = 25 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 72 nu = 0.247651 obj = -9.361125, rho = -0.534607 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 140 nu = 0.222671 obj = -10.716479, rho = -0.631398 nSV = 28, nBSV = 18 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 174 nu = 0.196571 obj = -12.322623, rho = -0.669440 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 70 nu = 0.176986 obj = -14.207339, rho = -0.729452 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 70 nu = 0.158896 obj = -16.437236, rho = -0.761667 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 73 nu = 0.146458 obj = -18.998754, rho = -0.739454 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 53 nu = 0.135099 obj = -21.818949, rho = -0.891887 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.120153 obj = -24.988983, rho = -1.013164 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 90 nu = 0.107280 obj = -28.753926, rho = -1.100424 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 47 nu = 0.100000 obj = -33.023371, rho = -1.390885 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .* optimization finished, #iter = 111 nu = 0.096643 obj = -36.830688, rho = -1.869500 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) .*.* optimization finished, #iter = 212 nu = 0.084284 obj = -40.328746, rho = -1.946434 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .* optimization finished, #iter = 188 nu = 0.072006 obj = -44.002307, rho = -2.003081 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 33 nu = 0.530536 obj = -3.680296, rho = 0.035514 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 60 nu = 0.471290 obj = -4.213787, rho = 0.053782 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 37 nu = 0.421948 obj = -4.853787, rho = 0.005540 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 41 nu = 0.392524 obj = -5.582787, rho = -0.101087 nSV = 42, nBSV = 37 Total nSV = 42 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 53 nu = 0.357509 obj = -6.344849, rho = -0.159794 nSV = 40, nBSV = 34 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 42 nu = 0.329383 obj = -7.127624, rho = -0.139756 nSV = 34, nBSV = 30 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 85 nu = 0.289853 obj = -7.918473, rho = -0.159870 nSV = 33, nBSV = 24 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 78 nu = 0.254653 obj = -8.802448, rho = -0.180762 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 56 nu = 0.223056 obj = -9.755979, rho = -0.241353 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 87 nu = 0.199645 obj = -10.644644, rho = -0.279736 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 80 nu = 0.172049 obj = -11.568202, rho = -0.213456 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 71 nu = 0.145297 obj = -12.535880, rho = -0.186705 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 93 nu = 0.125680 obj = -13.559276, rho = -0.253143 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .**.* optimization finished, #iter = 177 nu = 0.105267 obj = -14.608543, rho = -0.326041 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*...* optimization finished, #iter = 425 nu = 0.087422 obj = -15.844779, rho = -0.315003 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 258 nu = 0.073229 obj = -17.393603, rho = -0.308347 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 76 nu = 0.065289 obj = -19.079808, rho = -0.239508 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 82 nu = 0.058792 obj = -20.422798, rho = -0.083375 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.049676 obj = -21.361374, rho = -0.035281 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 93 nu = 0.041177 obj = -22.289715, rho = -0.100916 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 45 nu = 0.538356 obj = -3.679186, rho = -0.124780 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 44 nu = 0.485536 obj = -4.172013, rho = -0.097023 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 54 nu = 0.433462 obj = -4.721576, rho = -0.134574 nSV = 46, nBSV = 39 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 33 nu = 0.378093 obj = -5.368215, rho = -0.146773 nSV = 40, nBSV = 36 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 58 nu = 0.335966 obj = -6.129312, rho = -0.166211 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 41 nu = 0.299010 obj = -7.016357, rho = -0.158906 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 37 nu = 0.273221 obj = -8.037712, rho = -0.118434 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 83 nu = 0.253491 obj = -9.095291, rho = -0.059272 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 32 nu = 0.223464 obj = -10.226161, rho = -0.047913 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 73 nu = 0.201079 obj = -11.421385, rho = -0.140706 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 76 nu = 0.174005 obj = -12.721329, rho = -0.192036 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 90 nu = 0.149268 obj = -14.287004, rho = -0.190928 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 67 nu = 0.132580 obj = -16.078972, rho = -0.237526 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 60 nu = 0.118338 obj = -18.100881, rho = -0.340755 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.105329 obj = -20.231997, rho = -0.459259 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *...* optimization finished, #iter = 322 nu = 0.092186 obj = -22.496881, rho = -0.577587 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 255 nu = 0.078507 obj = -25.245164, rho = -0.592886 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 149 nu = 0.070891 obj = -28.334138, rho = -0.717258 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 97 nu = 0.065933 obj = -31.216780, rho = -0.747798 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.059811 obj = -33.298017, rho = -0.766098 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 63 nu = 0.649484 obj = -4.499190, rho = -0.109014 nSV = 69, nBSV = 63 Total nSV = 69 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 42 nu = 0.578767 obj = -5.157170, rho = -0.097407 nSV = 61, nBSV = 55 Total nSV = 61 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 76 nu = 0.519177 obj = -5.929653, rho = -0.172365 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 35 nu = 0.464168 obj = -6.838497, rho = -0.155965 nSV = 49, nBSV = 44 Total nSV = 49 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 38 nu = 0.425974 obj = -7.884454, rho = -0.069363 nSV = 45, nBSV = 41 Total nSV = 45 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 36 nu = 0.392939 obj = -9.021401, rho = -0.005752 nSV = 41, nBSV = 38 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 43 nu = 0.353337 obj = -10.251005, rho = -0.005495 nSV = 38, nBSV = 33 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 54 nu = 0.322336 obj = -11.575473, rho = -0.040767 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.280010 obj = -13.051876, rho = -0.045782 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.247984 obj = -14.773226, rho = -0.035140 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 120 nu = 0.220490 obj = -16.708809, rho = -0.048351 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 91 nu = 0.194609 obj = -18.915601, rho = -0.069767 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 146 nu = 0.172312 obj = -21.438850, rho = -0.090587 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.155485 obj = -24.311157, rho = -0.212810 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 164 nu = 0.136595 obj = -27.526069, rho = -0.273143 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.124422 obj = -31.174079, rho = -0.321036 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.112654 obj = -34.913056, rho = -0.273823 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) ...*.* optimization finished, #iter = 421 nu = 0.101433 obj = -38.448328, rho = -0.226168 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) ...*.* optimization finished, #iter = 446 nu = 0.085745 obj = -42.288037, rho = -0.208900 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) ..* optimization finished, #iter = 241 nu = 0.073565 obj = -46.832003, rho = -0.207590 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 40 nu = 0.601155 obj = -4.195555, rho = -0.058920 nSV = 62, nBSV = 58 Total nSV = 62 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 33 nu = 0.545291 obj = -4.788973, rho = -0.005735 nSV = 57, nBSV = 52 Total nSV = 57 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 36 nu = 0.485746 obj = -5.484385, rho = -0.002332 nSV = 51, nBSV = 47 Total nSV = 51 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 29 nu = 0.448501 obj = -6.249200, rho = -0.003555 nSV = 47, nBSV = 43 Total nSV = 47 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 41 nu = 0.399484 obj = -7.054872, rho = 0.052512 nSV = 44, nBSV = 36 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 44 nu = 0.352285 obj = -7.992723, rho = 0.035324 nSV = 38, nBSV = 33 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 83 nu = 0.315023 obj = -9.049329, rho = -0.023023 nSV = 35, nBSV = 27 Total nSV = 35 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 75 nu = 0.273179 obj = -10.297082, rho = -0.029385 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 64 nu = 0.244512 obj = -11.789123, rho = 0.036143 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 68 nu = 0.222672 obj = -13.507622, rho = 0.023706 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.196918 obj = -15.404723, rho = 0.060162 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 59 nu = 0.173237 obj = -17.714457, rho = 0.023577 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 50 nu = 0.159749 obj = -20.411182, rho = 0.050061 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.140505 obj = -23.525624, rho = 0.085717 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 115 nu = 0.125850 obj = -27.313494, rho = 0.138961 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 92 nu = 0.116799 obj = -31.774311, rho = 0.208096 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 149 nu = 0.110103 obj = -36.556360, rho = 0.333567 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 198 nu = 0.097089 obj = -41.744037, rho = 0.396690 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 177 nu = 0.085232 obj = -48.137328, rho = 0.450132 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 174 nu = 0.075619 obj = -56.110784, rho = 0.481171 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 38 nu = 0.546385 obj = -3.831277, rho = -0.068739 nSV = 57, nBSV = 51 Total nSV = 57 Accuracy = 95% (95/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 85 nu = 0.491046 obj = -4.404392, rho = -0.048567 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 95% (95/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 47 nu = 0.441450 obj = -5.074369, rho = -0.084498 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 95% (95/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 80 nu = 0.393796 obj = -5.866361, rho = -0.106575 nSV = 43, nBSV = 35 Total nSV = 43 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.354525 obj = -6.819096, rho = -0.146787 nSV = 39, nBSV = 32 Total nSV = 39 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 45 nu = 0.320847 obj = -7.959393, rho = -0.193514 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 67 nu = 0.302762 obj = -9.234022, rho = -0.250413 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 55 nu = 0.269412 obj = -10.706009, rho = -0.235545 nSV = 32, nBSV = 24 Total nSV = 32 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 52 nu = 0.249120 obj = -12.432687, rho = -0.168094 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 47 nu = 0.225589 obj = -14.412461, rho = -0.117208 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 78 nu = 0.204363 obj = -16.734687, rho = -0.075211 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.184829 obj = -19.460390, rho = -0.086599 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 97 nu = 0.168314 obj = -22.763224, rho = -0.039753 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 91 nu = 0.156648 obj = -26.494973, rho = -0.020236 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 98 nu = 0.144858 obj = -30.679035, rho = -0.108640 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 189 nu = 0.130112 obj = -35.494328, rho = -0.148373 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 163 nu = 0.117730 obj = -41.154503, rho = -0.204544 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) ...*...* optimization finished, #iter = 629 nu = 0.110322 obj = -47.385628, rho = -0.308488 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ...* optimization finished, #iter = 386 nu = 0.102495 obj = -53.914752, rho = -0.418243 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*.* optimization finished, #iter = 320 nu = 0.089188 obj = -61.268042, rho = -0.416826 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 35 nu = 0.548559 obj = -3.724871, rho = 0.050663 nSV = 57, nBSV = 53 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 36 nu = 0.502377 obj = -4.214704, rho = -0.013407 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 38 nu = 0.440890 obj = -4.742826, rho = -0.053735 nSV = 47, nBSV = 42 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 38 nu = 0.392438 obj = -5.334486, rho = -0.094320 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.351647 obj = -5.975264, rho = -0.070098 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 46 nu = 0.309043 obj = -6.658392, rho = -0.027272 nSV = 33, nBSV = 28 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 71 nu = 0.271742 obj = -7.405462, rho = 0.042177 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 64 nu = 0.237431 obj = -8.213025, rho = 0.082096 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 49 nu = 0.207821 obj = -9.109735, rho = 0.036410 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 59 nu = 0.178615 obj = -10.077291, rho = -0.000093 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.156478 obj = -11.153718, rho = -0.005009 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 57 nu = 0.136444 obj = -12.341331, rho = 0.013578 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 136 nu = 0.117133 obj = -13.625430, rho = 0.012933 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.100007 obj = -15.200224, rho = 0.029244 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.087740 obj = -16.997834, rho = 0.053183 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 218 nu = 0.077159 obj = -19.009882, rho = 0.103518 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 165 nu = 0.068272 obj = -21.247024, rho = 0.066410 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 87 nu = 0.062251 obj = -23.548497, rho = 0.029608 nSV = 9, nBSV = 3 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 194 nu = 0.056836 obj = -25.299671, rho = -0.016292 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 90 nu = 0.050321 obj = -26.498721, rho = -0.218457 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 85 nu = 0.587546 obj = -4.129786, rho = 0.000564 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 42 nu = 0.538805 obj = -4.742494, rho = -0.000768 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 61 nu = 0.487565 obj = -5.405539, rho = -0.078340 nSV = 52, nBSV = 46 Total nSV = 52 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 53 nu = 0.437486 obj = -6.146199, rho = -0.115827 nSV = 47, nBSV = 40 Total nSV = 47 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.396444 obj = -6.991884, rho = -0.195109 nSV = 44, nBSV = 36 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 40 nu = 0.349389 obj = -7.921628, rho = -0.179712 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 25 nu = 0.320000 obj = -8.950808, rho = -0.178636 nSV = 33, nBSV = 30 Total nSV = 33 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 41 nu = 0.282242 obj = -10.008133, rho = -0.221537 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 76 nu = 0.248498 obj = -11.174272, rho = -0.204290 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 66 nu = 0.216587 obj = -12.475460, rho = -0.194220 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 69 nu = 0.188026 obj = -13.963778, rho = -0.263151 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.168134 obj = -15.670806, rho = -0.152840 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 138 nu = 0.150673 obj = -17.407969, rho = -0.202872 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 200 nu = 0.132255 obj = -19.177718, rho = -0.178779 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *...* optimization finished, #iter = 385 nu = 0.114044 obj = -21.101897, rho = -0.096942 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *..* optimization finished, #iter = 227 nu = 0.097838 obj = -23.287657, rho = -0.037748 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.086970 obj = -25.567796, rho = -0.004951 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 147 nu = 0.077777 obj = -27.493105, rho = -0.045061 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 185 nu = 0.063561 obj = -29.423010, rho = -0.026649 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.053735 obj = -31.830298, rho = 0.025449 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 37 nu = 0.530885 obj = -3.499942, rho = 0.041134 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 52 nu = 0.471607 obj = -3.925848, rho = 0.035200 nSV = 50, nBSV = 43 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 33 nu = 0.418561 obj = -4.393775, rho = 0.003234 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 34 nu = 0.367183 obj = -4.905334, rho = 0.010658 nSV = 39, nBSV = 35 Total nSV = 39 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 52 nu = 0.320366 obj = -5.456781, rho = 0.042763 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 44 nu = 0.286013 obj = -6.074221, rho = 0.040678 nSV = 30, nBSV = 25 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.247069 obj = -6.734533, rho = 0.040689 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.212372 obj = -7.492429, rho = 0.066082 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 64 nu = 0.187068 obj = -8.352293, rho = 0.097276 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 49 nu = 0.169472 obj = -9.208475, rho = 0.171069 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 69 nu = 0.143652 obj = -10.121104, rho = 0.212365 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 73 nu = 0.129351 obj = -11.065613, rho = 0.320190 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.113165 obj = -11.833349, rho = 0.320017 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 143 nu = 0.094168 obj = -12.578770, rho = 0.305988 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 198 nu = 0.077419 obj = -13.394140, rho = 0.281608 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.067064 obj = -14.231545, rho = 0.298569 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 176 nu = 0.057523 obj = -14.814310, rho = 0.301482 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 242 nu = 0.048152 obj = -15.055419, rho = 0.287773 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) ..* optimization finished, #iter = 288 nu = 0.038460 obj = -15.093073, rho = 0.315942 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) ..* optimization finished, #iter = 288 nu = 0.030182 obj = -15.093073, rho = 0.315942 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 34 nu = 0.593008 obj = -4.081903, rho = -0.029473 nSV = 60, nBSV = 58 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 39 nu = 0.541493 obj = -4.641939, rho = -0.069195 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 42 nu = 0.496843 obj = -5.215965, rho = -0.023402 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.431818 obj = -5.821074, rho = -0.033307 nSV = 49, nBSV = 40 Total nSV = 49 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 87 nu = 0.384089 obj = -6.482122, rho = 0.011384 nSV = 42, nBSV = 34 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.329734 obj = -7.224162, rho = 0.017760 nSV = 39, nBSV = 29 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 95 nu = 0.287482 obj = -8.120539, rho = -0.001923 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 77 nu = 0.255083 obj = -9.110345, rho = -0.113213 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 65 nu = 0.225406 obj = -10.192978, rho = -0.083033 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 73 nu = 0.203074 obj = -11.368200, rho = -0.096263 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.178551 obj = -12.496814, rho = -0.075065 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.155833 obj = -13.656107, rho = -0.131648 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 155 nu = 0.132703 obj = -14.890420, rho = -0.127911 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ...*..* optimization finished, #iter = 509 nu = 0.118217 obj = -16.136222, rho = -0.190767 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*..* optimization finished, #iter = 343 nu = 0.097141 obj = -17.403159, rho = -0.186825 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 91 nu = 0.086371 obj = -18.805975, rho = -0.268223 nSV = 12, nBSV = 7 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 198 nu = 0.075437 obj = -19.713189, rho = -0.241412 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*.* optimization finished, #iter = 226 nu = 0.060635 obj = -20.526102, rho = -0.237062 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*.* optimization finished, #iter = 234 nu = 0.049273 obj = -21.476468, rho = -0.259234 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*.* optimization finished, #iter = 250 nu = 0.041375 obj = -22.531129, rho = -0.160029 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 58 nu = 0.615057 obj = -4.125897, rho = -0.150878 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 43 nu = 0.550254 obj = -4.666071, rho = -0.185853 nSV = 58, nBSV = 50 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 44 nu = 0.486678 obj = -5.277712, rho = -0.167850 nSV = 51, nBSV = 46 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 74 nu = 0.427835 obj = -5.964141, rho = -0.198458 nSV = 47, nBSV = 41 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 37 nu = 0.380233 obj = -6.761675, rho = -0.200952 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 72 nu = 0.343369 obj = -7.625993, rho = -0.130540 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 92 nu = 0.298035 obj = -8.606099, rho = -0.114793 nSV = 36, nBSV = 26 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 155 nu = 0.264852 obj = -9.762104, rho = -0.157291 nSV = 31, nBSV = 23 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.232317 obj = -11.091421, rho = -0.180846 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 37 nu = 0.212741 obj = -12.635061, rho = -0.279112 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 49 nu = 0.190822 obj = -14.215196, rho = -0.317184 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 57 nu = 0.166156 obj = -16.050069, rho = -0.352750 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 61 nu = 0.151093 obj = -18.083077, rho = -0.366060 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 98 nu = 0.132407 obj = -20.233459, rho = -0.373929 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.117600 obj = -22.639762, rho = -0.363848 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 41 nu = 0.104976 obj = -25.213356, rho = -0.400084 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 211 nu = 0.091814 obj = -27.854202, rho = -0.415317 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 137 nu = 0.078947 obj = -30.784076, rho = -0.386315 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.069184 obj = -34.043749, rho = -0.319722 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.061746 obj = -37.333874, rho = -0.267187 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 37 nu = 0.554214 obj = -3.647386, rho = -0.145926 nSV = 57, nBSV = 54 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 71 nu = 0.493398 obj = -4.080888, rho = -0.134426 nSV = 53, nBSV = 45 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 45 nu = 0.434900 obj = -4.569901, rho = -0.175579 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 53 nu = 0.382242 obj = -5.099838, rho = -0.158604 nSV = 42, nBSV = 36 Total nSV = 42 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 38 nu = 0.330818 obj = -5.701318, rho = -0.151216 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 83 nu = 0.292358 obj = -6.350840, rho = -0.134774 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 43 nu = 0.258187 obj = -7.105321, rho = -0.154056 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 60 nu = 0.231042 obj = -7.849133, rho = -0.212232 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 58 nu = 0.201522 obj = -8.635373, rho = -0.257295 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 80 nu = 0.172971 obj = -9.469846, rho = -0.297462 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 73 nu = 0.149042 obj = -10.387140, rho = -0.300203 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 88 nu = 0.126776 obj = -11.432203, rho = -0.257472 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 71 nu = 0.116981 obj = -12.459667, rho = -0.286093 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*..* optimization finished, #iter = 311 nu = 0.101010 obj = -13.199604, rho = -0.292346 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.............................................* optimization finished, #iter = 4690 nu = 0.082655 obj = -13.955036, rho = -0.285234 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.................* optimization finished, #iter = 1889 nu = 0.068934 obj = -14.712833, rho = -0.271361 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 246 nu = 0.057360 obj = -15.555623, rho = -0.273160 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 131 nu = 0.051564 obj = -16.064515, rho = -0.292926 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 210 nu = 0.040957 obj = -16.068400, rho = -0.295066 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 210 nu = 0.032141 obj = -16.068400, rho = -0.295066 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 53 nu = 0.588284 obj = -3.933428, rho = -0.252545 nSV = 62, nBSV = 56 Total nSV = 62 Accuracy = 96% (96/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 50 nu = 0.523210 obj = -4.443590, rho = -0.284030 nSV = 54, nBSV = 49 Total nSV = 54 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 63 nu = 0.462509 obj = -5.019472, rho = -0.294894 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 97% (97/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.408206 obj = -5.669705, rho = -0.267861 nSV = 45, nBSV = 37 Total nSV = 45 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 86 nu = 0.363338 obj = -6.422305, rho = -0.310689 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 48 nu = 0.318632 obj = -7.278205, rho = -0.333799 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 53 nu = 0.281413 obj = -8.297959, rho = -0.340068 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 37 nu = 0.250810 obj = -9.508774, rho = -0.287049 nSV = 29, nBSV = 23 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 48 nu = 0.226621 obj = -10.846796, rho = -0.238211 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.201469 obj = -12.416125, rho = -0.245631 nSV = 22, nBSV = 17 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 63 nu = 0.181346 obj = -14.252716, rho = -0.291855 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 61 nu = 0.162619 obj = -16.346338, rho = -0.406402 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.146401 obj = -18.772571, rho = -0.459380 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 139 nu = 0.133189 obj = -21.556464, rho = -0.432617 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.118389 obj = -24.632364, rho = -0.332434 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..* optimization finished, #iter = 226 nu = 0.106699 obj = -28.329257, rho = -0.149564 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*..* optimization finished, #iter = 316 nu = 0.094377 obj = -32.653722, rho = -0.104933 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.085932 obj = -37.898297, rho = -0.123664 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 266 nu = 0.077585 obj = -43.740031, rho = -0.127031 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 268 nu = 0.068291 obj = -51.012331, rho = -0.101628 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 32 nu = 0.540000 obj = -3.732784, rho = -0.201566 nSV = 55, nBSV = 53 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 38 nu = 0.488847 obj = -4.250498, rho = -0.177769 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 29 nu = 0.447402 obj = -4.823785, rho = -0.127559 nSV = 46, nBSV = 43 Total nSV = 46 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 59 nu = 0.398993 obj = -5.416487, rho = -0.054774 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 49 nu = 0.348019 obj = -6.093079, rho = -0.060782 nSV = 37, nBSV = 31 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 61 nu = 0.313193 obj = -6.829473, rho = 0.013122 nSV = 36, nBSV = 27 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 35 nu = 0.275346 obj = -7.638218, rho = 0.033723 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 57 nu = 0.238469 obj = -8.546395, rho = 0.070348 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 43 nu = 0.222652 obj = -9.501922, rho = 0.013375 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 68 nu = 0.190664 obj = -10.402296, rho = -0.019201 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.163491 obj = -11.382451, rho = -0.037338 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.139287 obj = -12.499681, rho = -0.001438 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 52 nu = 0.122182 obj = -13.701497, rho = 0.034561 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 91 nu = 0.105191 obj = -14.929288, rho = -0.057677 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 79 nu = 0.091289 obj = -16.261065, rho = -0.171189 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 143 nu = 0.078184 obj = -17.503288, rho = -0.144718 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 135 nu = 0.065581 obj = -18.891965, rho = -0.133565 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.055581 obj = -20.433066, rho = -0.146647 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 93 nu = 0.050193 obj = -21.910739, rho = -0.227027 nSV = 8, nBSV = 3 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 125 nu = 0.044758 obj = -22.382584, rho = -0.337882 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 40 nu = 0.590410 obj = -3.892649, rho = -0.017500 nSV = 61, nBSV = 57 Total nSV = 61 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 46 nu = 0.523794 obj = -4.365074, rho = 0.017784 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 46 nu = 0.463930 obj = -4.885554, rho = 0.017819 nSV = 49, nBSV = 43 Total nSV = 49 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 56 nu = 0.399353 obj = -5.480067, rho = 0.002397 nSV = 45, nBSV = 36 Total nSV = 45 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 48 nu = 0.351208 obj = -6.168231, rho = 0.024751 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 30 nu = 0.318058 obj = -6.935719, rho = -0.006221 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 49 nu = 0.275680 obj = -7.770216, rho = 0.030488 nSV = 32, nBSV = 25 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 79 nu = 0.244653 obj = -8.722230, rho = 0.075901 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 47 nu = 0.216502 obj = -9.763086, rho = 0.058979 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 52 nu = 0.188787 obj = -10.901171, rho = 0.005899 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 146 nu = 0.165920 obj = -12.199161, rho = 0.001071 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 66 nu = 0.142093 obj = -13.734487, rho = -0.021851 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 51 nu = 0.124300 obj = -15.621928, rho = -0.002474 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 46 nu = 0.110683 obj = -17.856638, rho = 0.032306 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 64 nu = 0.102040 obj = -20.290969, rho = 0.064019 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 57 nu = 0.090644 obj = -22.963090, rho = 0.004828 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 77 nu = 0.083166 obj = -25.743825, rho = -0.065869 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 70 nu = 0.074355 obj = -28.492277, rho = -0.010672 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.067220 obj = -30.909558, rho = 0.061926 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 179 nu = 0.056420 obj = -33.151724, rho = 0.071838 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 48 nu = 0.543747 obj = -3.523142, rho = -0.086800 nSV = 58, nBSV = 52 Total nSV = 58 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.476999 obj = -3.933820, rho = -0.123675 nSV = 50, nBSV = 45 Total nSV = 50 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 30 nu = 0.424873 obj = -4.373332, rho = -0.146673 nSV = 46, nBSV = 41 Total nSV = 46 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 65 nu = 0.370825 obj = -4.823561, rho = -0.167484 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 36 nu = 0.327722 obj = -5.316314, rho = -0.222927 nSV = 35, nBSV = 29 Total nSV = 35 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 47 nu = 0.283197 obj = -5.791967, rho = -0.258629 nSV = 32, nBSV = 23 Total nSV = 32 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 36 nu = 0.238943 obj = -6.327900, rho = -0.256672 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.209637 obj = -6.910010, rho = -0.234640 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 91 nu = 0.180219 obj = -7.503978, rho = -0.267146 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *..* optimization finished, #iter = 250 nu = 0.151575 obj = -8.154832, rho = -0.231033 nSV = 21, nBSV = 10 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.*.* optimization finished, #iter = 197 nu = 0.127973 obj = -8.913550, rho = -0.285239 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 50 nu = 0.117729 obj = -9.636409, rho = -0.431091 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.099475 obj = -10.111166, rho = -0.474579 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 93 nu = 0.081843 obj = -10.616218, rho = -0.488559 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 87 nu = 0.068484 obj = -11.126536, rho = -0.441276 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) .*.* optimization finished, #iter = 233 nu = 0.058261 obj = -11.470981, rho = -0.465165 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*....* optimization finished, #iter = 548 nu = 0.046845 obj = -11.663265, rho = -0.484873 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*.* optimization finished, #iter = 211 nu = 0.037313 obj = -11.829576, rho = -0.491892 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*.* optimization finished, #iter = 314 nu = 0.030369 obj = -11.917989, rho = -0.499172 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) ..*.* optimization finished, #iter = 314 nu = 0.023832 obj = -11.917989, rho = -0.499172 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 47 nu = 0.549866 obj = -3.600033, rho = -0.243344 nSV = 57, nBSV = 50 Total nSV = 57 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 57 nu = 0.493472 obj = -4.005649, rho = -0.181959 nSV = 53, nBSV = 46 Total nSV = 53 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 38 nu = 0.422084 obj = -4.463774, rho = -0.177900 nSV = 47, nBSV = 40 Total nSV = 47 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 61 nu = 0.377744 obj = -4.969148, rho = -0.110083 nSV = 41, nBSV = 35 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 48 nu = 0.331213 obj = -5.490756, rho = -0.070961 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 63 nu = 0.286802 obj = -6.079193, rho = -0.078449 nSV = 31, nBSV = 26 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 59 nu = 0.247870 obj = -6.730118, rho = -0.103467 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 21 nu = 0.220000 obj = -7.427902, rho = -0.146749 nSV = 24, nBSV = 21 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 70 nu = 0.189710 obj = -8.127158, rho = -0.174163 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 94 nu = 0.163618 obj = -8.879851, rho = -0.196074 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.143891 obj = -9.690692, rho = -0.230003 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *..* optimization finished, #iter = 236 nu = 0.125689 obj = -10.367050, rho = -0.280486 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*...* optimization finished, #iter = 404 nu = 0.105674 obj = -11.061577, rho = -0.291149 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *....* optimization finished, #iter = 472 nu = 0.089467 obj = -11.642221, rho = -0.311507 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*........* optimization finished, #iter = 953 nu = 0.072595 obj = -12.290549, rho = -0.305060 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 294 nu = 0.060121 obj = -13.045566, rho = -0.253870 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.049205 obj = -13.930201, rho = -0.246609 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 176 nu = 0.040501 obj = -15.044220, rho = -0.241392 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.034173 obj = -16.429279, rho = -0.257622 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 99 nu = 0.030143 obj = -17.884671, rho = -0.316708 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 49 nu = 0.588078 obj = -3.860148, rho = 0.093994 nSV = 62, nBSV = 55 Total nSV = 62 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 60 nu = 0.526774 obj = -4.332385, rho = 0.038570 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 71 nu = 0.462165 obj = -4.822162, rho = 0.013621 nSV = 50, nBSV = 43 Total nSV = 50 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.401464 obj = -5.376660, rho = 0.046401 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 78 nu = 0.353348 obj = -5.999685, rho = 0.051891 nSV = 38, nBSV = 32 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 51 nu = 0.312896 obj = -6.681445, rho = 0.040141 nSV = 34, nBSV = 28 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.276755 obj = -7.378760, rho = 0.073306 nSV = 31, nBSV = 22 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.233348 obj = -8.159083, rho = 0.078984 nSV = 29, nBSV = 19 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 78 nu = 0.199744 obj = -9.118060, rho = 0.049452 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 60 nu = 0.176482 obj = -10.234238, rho = 0.102501 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.153770 obj = -11.509841, rho = 0.089783 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 219 nu = 0.133425 obj = -13.006588, rho = 0.091789 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 157 nu = 0.118449 obj = -14.817262, rho = 0.036496 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 140 nu = 0.107166 obj = -16.791999, rho = 0.003096 nSV = 18, nBSV = 7 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) *...* optimization finished, #iter = 318 nu = 0.092436 obj = -19.095629, rho = 0.021633 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 82 nu = 0.082955 obj = -21.932189, rho = -0.012478 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 96 nu = 0.077472 obj = -24.967272, rho = -0.111679 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*.* optimization finished, #iter = 324 nu = 0.070009 obj = -28.016422, rho = -0.280388 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*.* optimization finished, #iter = 218 nu = 0.060325 obj = -31.492157, rho = -0.326198 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 163 nu = 0.052613 obj = -35.720953, rho = -0.313196 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 61 nu = 0.580048 obj = -3.928970, rho = -0.029208 nSV = 60, nBSV = 55 Total nSV = 60 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 39 nu = 0.514843 obj = -4.461737, rho = -0.027604 nSV = 55, nBSV = 49 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 46 nu = 0.458785 obj = -5.070969, rho = 0.000049 nSV = 50, nBSV = 44 Total nSV = 50 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 36 nu = 0.415592 obj = -5.738961, rho = -0.073025 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 65 nu = 0.368772 obj = -6.484082, rho = -0.049800 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 45 nu = 0.326539 obj = -7.304170, rho = -0.043580 nSV = 38, nBSV = 28 Total nSV = 38 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 99 nu = 0.284706 obj = -8.267949, rho = -0.044693 nSV = 34, nBSV = 24 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.252711 obj = -9.396909, rho = -0.042193 nSV = 30, nBSV = 21 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 52 nu = 0.228224 obj = -10.691239, rho = -0.004882 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.204640 obj = -12.039994, rho = -0.010817 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 61 nu = 0.178870 obj = -13.612690, rho = -0.055066 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 112 nu = 0.155911 obj = -15.485310, rho = -0.022663 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 96 nu = 0.138376 obj = -17.754291, rho = -0.055816 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 78 nu = 0.125939 obj = -20.336089, rho = -0.051826 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 65 nu = 0.115332 obj = -23.175933, rho = 0.020726 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.104326 obj = -26.161428, rho = 0.063830 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 191 nu = 0.093628 obj = -29.408844, rho = 0.128545 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 175 nu = 0.088154 obj = -32.433449, rho = -0.009431 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) .* optimization finished, #iter = 172 nu = 0.078513 obj = -34.454068, rho = -0.245395 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 168 nu = 0.068065 obj = -35.863222, rho = -0.557581 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 29 nu = 0.553599 obj = -3.866720, rho = -0.147132 nSV = 56, nBSV = 54 Total nSV = 56 Accuracy = 96% (96/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 41 nu = 0.491877 obj = -4.448108, rho = -0.108119 nSV = 54, nBSV = 48 Total nSV = 54 Accuracy = 95% (95/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 39 nu = 0.442436 obj = -5.131763, rho = -0.100670 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 95% (95/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 31 nu = 0.405512 obj = -5.919359, rho = -0.076153 nSV = 43, nBSV = 38 Total nSV = 43 Accuracy = 95% (95/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 88 nu = 0.366585 obj = -6.789517, rho = -0.106434 nSV = 40, nBSV = 32 Total nSV = 40 Accuracy = 95% (95/100) (classification) Accuracy = 96.3% (963/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.326731 obj = -7.833353, rho = -0.135488 nSV = 36, nBSV = 27 Total nSV = 36 Accuracy = 93% (93/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 163 nu = 0.289162 obj = -9.098019, rho = -0.155516 nSV = 34, nBSV = 24 Total nSV = 34 Accuracy = 93% (93/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 50 nu = 0.260593 obj = -10.667765, rho = -0.153923 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 95% (95/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 84 nu = 0.236327 obj = -12.581163, rho = -0.130724 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 95% (95/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 160 nu = 0.215603 obj = -14.933182, rho = -0.112552 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 96% (96/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 65 nu = 0.199319 obj = -17.824842, rho = -0.109559 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 46 nu = 0.191767 obj = -21.261054, rho = -0.223410 nSV = 22, nBSV = 17 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 64 nu = 0.185212 obj = -25.016649, rho = -0.346303 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 192 nu = 0.171809 obj = -29.116718, rho = -0.279559 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..* optimization finished, #iter = 264 nu = 0.154054 obj = -33.991748, rho = -0.233970 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*.* optimization finished, #iter = 313 nu = 0.140309 obj = -39.888110, rho = -0.141912 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*..* optimization finished, #iter = 331 nu = 0.128574 obj = -46.935009, rho = -0.130472 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) ..*.....* optimization finished, #iter = 709 nu = 0.119509 obj = -55.211773, rho = -0.148071 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 96.1% (961/1000) (classification) .*..* optimization finished, #iter = 344 nu = 0.112332 obj = -64.448069, rho = -0.307036 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 95.9% (959/1000) (classification) .* optimization finished, #iter = 155 nu = 0.101763 obj = -75.332079, rho = -0.415236 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 48 nu = 0.613319 obj = -4.255798, rho = -0.069522 nSV = 64, nBSV = 59 Total nSV = 64 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 45 nu = 0.551071 obj = -4.866341, rho = -0.079125 nSV = 58, nBSV = 53 Total nSV = 58 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 33 nu = 0.500000 obj = -5.566126, rho = -0.076431 nSV = 52, nBSV = 49 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 48 nu = 0.460000 obj = -6.309017, rho = -0.144257 nSV = 48, nBSV = 44 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.405616 obj = -7.099723, rho = -0.144116 nSV = 44, nBSV = 38 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 75 nu = 0.358722 obj = -8.006965, rho = -0.148485 nSV = 39, nBSV = 31 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 86 nu = 0.323403 obj = -8.958619, rho = -0.247914 nSV = 36, nBSV = 27 Total nSV = 36 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.281634 obj = -9.993702, rho = -0.256564 nSV = 34, nBSV = 24 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.....* optimization finished, #iter = 520 nu = 0.244965 obj = -11.172486, rho = -0.299569 nSV = 29, nBSV = 20 Total nSV = 29 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.214947 obj = -12.570127, rho = -0.337487 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.187131 obj = -14.173265, rho = -0.215301 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 75 nu = 0.165697 obj = -16.057978, rho = -0.246216 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 82 nu = 0.143585 obj = -18.284659, rho = -0.282922 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 38 nu = 0.130563 obj = -20.918903, rho = -0.379856 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.123016 obj = -23.632807, rho = -0.333496 nSV = 15, nBSV = 10 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 92 nu = 0.108124 obj = -26.271834, rho = -0.325227 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 94 nu = 0.091715 obj = -29.414757, rho = -0.298696 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 90 nu = 0.079363 obj = -33.356516, rho = -0.290742 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 160 nu = 0.075244 obj = -37.533128, rho = -0.495404 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 147 nu = 0.068133 obj = -41.021420, rho = -0.662368 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 36 nu = 0.558158 obj = -3.713288, rho = -0.027438 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 63 nu = 0.487841 obj = -4.196681, rho = -0.045948 nSV = 54, nBSV = 46 Total nSV = 54 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 46 nu = 0.440000 obj = -4.748882, rho = -0.115334 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 37 nu = 0.384610 obj = -5.371391, rho = -0.106111 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 47 nu = 0.341023 obj = -6.101168, rho = -0.114118 nSV = 37, nBSV = 29 Total nSV = 37 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 59 nu = 0.302405 obj = -6.955608, rho = -0.086081 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 44 nu = 0.268954 obj = -7.947340, rho = -0.046377 nSV = 31, nBSV = 25 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 62 nu = 0.244483 obj = -9.034385, rho = 0.010807 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 47 nu = 0.217007 obj = -10.276683, rho = 0.051258 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 52 nu = 0.190211 obj = -11.723143, rho = 0.087632 nSV = 22, nBSV = 17 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 56 nu = 0.176342 obj = -13.356234, rho = 0.155073 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 93 nu = 0.162545 obj = -14.993415, rho = 0.236657 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.*.* optimization finished, #iter = 208 nu = 0.143943 obj = -16.656363, rho = 0.234250 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.126163 obj = -18.406900, rho = 0.132365 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.108785 obj = -20.266047, rho = 0.108631 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.091312 obj = -22.498575, rho = 0.111862 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 92 nu = 0.082029 obj = -25.127522, rho = 0.168494 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 88 nu = 0.076165 obj = -27.434714, rho = 0.206023 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*.* optimization finished, #iter = 342 nu = 0.064453 obj = -29.243322, rho = 0.247461 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ...*....*...* optimization finished, #iter = 985 nu = 0.054030 obj = -31.178557, rho = 0.287104 nSV = 13, nBSV = 1 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 39 nu = 0.626973 obj = -4.509940, rho = -0.253803 nSV = 65, nBSV = 61 Total nSV = 65 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 35 nu = 0.580000 obj = -5.217246, rho = -0.266227 nSV = 60, nBSV = 56 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 39 nu = 0.540631 obj = -5.973188, rho = -0.206568 nSV = 56, nBSV = 51 Total nSV = 56 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 40 nu = 0.486275 obj = -6.780482, rho = -0.207549 nSV = 52, nBSV = 47 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 71 nu = 0.432704 obj = -7.661158, rho = -0.203387 nSV = 45, nBSV = 40 Total nSV = 45 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 41 nu = 0.382067 obj = -8.694292, rho = -0.223341 nSV = 41, nBSV = 36 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 39 nu = 0.345134 obj = -9.844227, rho = -0.240221 nSV = 37, nBSV = 32 Total nSV = 37 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 33 nu = 0.309281 obj = -11.079909, rho = -0.286366 nSV = 34, nBSV = 29 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 76 nu = 0.279014 obj = -12.367021, rho = -0.298107 nSV = 30, nBSV = 24 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.244328 obj = -13.685408, rho = -0.295730 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 146 nu = 0.212938 obj = -15.126596, rho = -0.284901 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.181444 obj = -16.783049, rho = -0.275073 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 70 nu = 0.160151 obj = -18.713432, rho = -0.367064 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.141313 obj = -20.706304, rho = -0.462829 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.127924 obj = -22.589195, rho = -0.593849 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 151 nu = 0.108629 obj = -24.381071, rho = -0.522159 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ..* optimization finished, #iter = 299 nu = 0.094975 obj = -26.042840, rho = -0.653798 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) ..*.* optimization finished, #iter = 316 nu = 0.078733 obj = -27.595081, rho = -0.791435 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) ....*..* optimization finished, #iter = 691 nu = 0.067261 obj = -28.979833, rho = -0.902537 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..*...* optimization finished, #iter = 510 nu = 0.054358 obj = -30.376303, rho = -0.909044 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 60 nu = 0.537478 obj = -3.434511, rho = -0.016908 nSV = 57, nBSV = 49 Total nSV = 57 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 43 nu = 0.473612 obj = -3.806589, rho = 0.054388 nSV = 51, nBSV = 45 Total nSV = 51 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 38 nu = 0.411597 obj = -4.216023, rho = 0.053809 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 69 nu = 0.359124 obj = -4.645352, rho = 0.036389 nSV = 41, nBSV = 33 Total nSV = 41 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 39 nu = 0.310735 obj = -5.129453, rho = 0.050018 nSV = 33, nBSV = 27 Total nSV = 33 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 37 nu = 0.268239 obj = -5.645214, rho = 0.063978 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 58 nu = 0.229747 obj = -6.245588, rho = 0.036375 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 62 nu = 0.198410 obj = -6.930451, rho = 0.015027 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.174840 obj = -7.690365, rho = -0.005168 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 180 nu = 0.151375 obj = -8.527119, rho = -0.021764 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.131256 obj = -9.476386, rho = -0.069818 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 130 nu = 0.113465 obj = -10.557294, rho = -0.037799 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 59 nu = 0.098000 obj = -11.838283, rho = 0.002450 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 63 nu = 0.087333 obj = -13.283531, rho = 0.088070 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 63 nu = 0.079502 obj = -14.763209, rho = 0.170465 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.069865 obj = -16.157816, rho = 0.237124 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 158 nu = 0.060927 obj = -17.564778, rho = 0.280473 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 143 nu = 0.053652 obj = -18.850622, rho = 0.296254 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 171 nu = 0.046033 obj = -19.878113, rho = 0.359732 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..*.* optimization finished, #iter = 313 nu = 0.039888 obj = -20.486250, rho = 0.409672 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 35 nu = 0.560631 obj = -3.773059, rho = -0.140507 nSV = 60, nBSV = 54 Total nSV = 60 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 42 nu = 0.496429 obj = -4.273343, rho = -0.126973 nSV = 52, nBSV = 48 Total nSV = 52 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 56 nu = 0.449106 obj = -4.826562, rho = -0.132761 nSV = 46, nBSV = 42 Total nSV = 46 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 54 nu = 0.394251 obj = -5.439508, rho = -0.081459 nSV = 43, nBSV = 36 Total nSV = 43 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 50 nu = 0.346990 obj = -6.155328, rho = -0.056960 nSV = 39, nBSV = 33 Total nSV = 39 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 196 nu = 0.312294 obj = -6.935967, rho = -0.020859 nSV = 34, nBSV = 27 Total nSV = 34 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 49 nu = 0.280000 obj = -7.813781, rho = 0.013789 nSV = 30, nBSV = 27 Total nSV = 30 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 43 nu = 0.249221 obj = -8.721683, rho = 0.045592 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 56 nu = 0.216531 obj = -9.723131, rho = 0.057658 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 85 nu = 0.188130 obj = -10.858657, rho = 0.063404 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 58 nu = 0.162095 obj = -12.220930, rho = 0.076860 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 67 nu = 0.146777 obj = -13.746551, rho = 0.096626 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 86 nu = 0.135714 obj = -15.222819, rho = 0.028401 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 242 nu = 0.122808 obj = -16.416085, rho = -0.015016 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 161 nu = 0.101169 obj = -17.510704, rho = -0.012156 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) ..*...* optimization finished, #iter = 564 nu = 0.086604 obj = -18.604087, rho = -0.049418 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) ...*..* optimization finished, #iter = 594 nu = 0.070971 obj = -19.787379, rho = -0.065118 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*......* optimization finished, #iter = 701 nu = 0.059205 obj = -21.127569, rho = -0.082342 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 253 nu = 0.049536 obj = -22.668622, rho = -0.107300 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.042887 obj = -24.131177, rho = -0.050476 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 40 nu = 0.578097 obj = -3.929299, rho = -0.091021 nSV = 61, nBSV = 56 Total nSV = 61 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 38 nu = 0.524649 obj = -4.454167, rho = -0.130644 nSV = 55, nBSV = 50 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 74 nu = 0.467500 obj = -5.008201, rho = -0.124781 nSV = 51, nBSV = 42 Total nSV = 51 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 42 nu = 0.418414 obj = -5.625804, rho = -0.055110 nSV = 43, nBSV = 39 Total nSV = 43 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 59 nu = 0.372724 obj = -6.261825, rho = -0.048003 nSV = 41, nBSV = 34 Total nSV = 41 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 74 nu = 0.331483 obj = -6.910793, rho = -0.097420 nSV = 36, nBSV = 29 Total nSV = 36 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 80 nu = 0.282833 obj = -7.623198, rho = -0.103611 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 72 nu = 0.254227 obj = -8.402352, rho = -0.030745 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.217541 obj = -9.130427, rho = 0.009619 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.184900 obj = -9.931959, rho = 0.042476 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 79 nu = 0.166269 obj = -10.721912, rho = -0.105760 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.140969 obj = -11.329624, rho = -0.131378 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 295 nu = 0.116377 obj = -11.919966, rho = -0.090118 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 220 nu = 0.096365 obj = -12.527919, rho = -0.112028 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ...*.* optimization finished, #iter = 457 nu = 0.078817 obj = -13.197744, rho = -0.145494 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 89 nu = 0.067495 obj = -13.880403, rho = -0.062186 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.054870 obj = -14.412806, rho = -0.021631 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 86 nu = 0.045266 obj = -14.974353, rho = 0.055656 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.037659 obj = -15.334390, rho = 0.155561 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.030494 obj = -15.601830, rho = 0.115037 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 67 nu = 0.564667 obj = -3.879609, rho = -0.092367 nSV = 59, nBSV = 53 Total nSV = 59 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.500896 obj = -4.435864, rho = -0.067622 nSV = 55, nBSV = 46 Total nSV = 55 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 54 nu = 0.441703 obj = -5.103621, rho = -0.053977 nSV = 48, nBSV = 43 Total nSV = 48 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 36 nu = 0.406299 obj = -5.881609, rho = -0.012814 nSV = 44, nBSV = 40 Total nSV = 44 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 27 nu = 0.373327 obj = -6.727152, rho = -0.038989 nSV = 39, nBSV = 36 Total nSV = 39 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 49 nu = 0.342420 obj = -7.601573, rho = 0.066760 nSV = 38, nBSV = 31 Total nSV = 38 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 56 nu = 0.304626 obj = -8.541671, rho = 0.084930 nSV = 33, nBSV = 26 Total nSV = 33 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 67 nu = 0.267570 obj = -9.566762, rho = 0.054006 nSV = 31, nBSV = 24 Total nSV = 31 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 64 nu = 0.232539 obj = -10.747175, rho = 0.021426 nSV = 28, nBSV = 22 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 97 nu = 0.203583 obj = -12.125552, rho = 0.069381 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 44 nu = 0.181437 obj = -13.764179, rho = 0.124849 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 48 nu = 0.168601 obj = -15.421498, rho = 0.082545 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 87 nu = 0.147119 obj = -17.098866, rho = 0.012801 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .* optimization finished, #iter = 189 nu = 0.128630 obj = -18.852845, rho = -0.078502 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*..* optimization finished, #iter = 375 nu = 0.112015 obj = -20.722583, rho = -0.035075 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 178 nu = 0.099555 obj = -22.600354, rho = -0.098112 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.085054 obj = -24.438932, rho = -0.082783 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 177 nu = 0.071905 obj = -26.434064, rho = -0.132896 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) ..*.* optimization finished, #iter = 351 nu = 0.060654 obj = -28.684254, rho = -0.218991 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 160 nu = 0.051641 obj = -31.165318, rho = -0.307785 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 33 nu = 0.520000 obj = -3.514528, rho = -0.168534 nSV = 55, nBSV = 51 Total nSV = 55 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 25 nu = 0.464138 obj = -3.981207, rho = -0.215138 nSV = 48, nBSV = 46 Total nSV = 48 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 42 nu = 0.423135 obj = -4.485843, rho = -0.208470 nSV = 44, nBSV = 39 Total nSV = 44 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 33 nu = 0.375510 obj = -5.012786, rho = -0.149923 nSV = 40, nBSV = 33 Total nSV = 40 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 52 nu = 0.324709 obj = -5.614481, rho = -0.191447 nSV = 37, nBSV = 30 Total nSV = 37 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 35 nu = 0.290615 obj = -6.306227, rho = -0.242043 nSV = 30, nBSV = 26 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 59 nu = 0.251671 obj = -7.050572, rho = -0.204931 nSV = 30, nBSV = 22 Total nSV = 30 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 49 nu = 0.220488 obj = -7.899226, rho = -0.156641 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 26 nu = 0.198820 obj = -8.874888, rho = -0.213776 nSV = 21, nBSV = 18 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 48 nu = 0.179192 obj = -9.769118, rho = -0.203241 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.155871 obj = -10.687263, rho = -0.230823 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 88 nu = 0.135978 obj = -11.603449, rho = -0.308501 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 96 nu = 0.114874 obj = -12.517105, rho = -0.346174 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 157 nu = 0.097408 obj = -13.564327, rho = -0.428449 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 184 nu = 0.084997 obj = -14.573436, rho = -0.436928 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 168 nu = 0.071938 obj = -15.493243, rho = -0.432280 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 160 nu = 0.059010 obj = -16.450390, rho = -0.427360 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 156 nu = 0.048715 obj = -17.618253, rho = -0.445880 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 141 nu = 0.040695 obj = -18.985377, rho = -0.478730 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 246 nu = 0.034183 obj = -20.541190, rho = -0.496419 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification)
In [ ]:
import numpy as np
import numpy.matlib as matlib
from libsvm.svmutil import *
import matplotlib.pyplot as plt
def data(N,sigma):
w = np.ones(10)/np.sqrt(10)
w1 = [1., 1., 1., 1., 1., -1., -1., -1., -1., -1.]/np.sqrt(10)
w2 = [-1., -1., 0, 1., 1., -1., -1., 0, -1., -1.]/np.sqrt(8)
x = np.zeros((4,10))
x[1,:] = x[0,:] + sigma*w1
x[2,:] = x[0,:] + sigma*w2
x[3,:] = x[2,:] + sigma*w1
X1 = x + sigma*matlib.repmat(w,4,1)/2
X2 = x - sigma*matlib.repmat(w,4,1)/2
X1 = matlib.repmat(X1,2*N,1)
X2 = matlib.repmat(X2,2*N,1)
X = np.concatenate((X1, X2), axis=0)
Y = np.concatenate((np.ones(4*2*N), -np.ones(4*2*N)),axis=0)
Z = np.random.permutation(16*N)
Z = Z[:N]
X = X[Z,:]
X = X + 0.2*sigma*np.random.randn(N,10)
Y = Y[Z]
return X, Y
# Task 2a: Generating Parameter Values
lambda_values = np.logspace(-1, 1, 20) # Logarithmically spaced values between 0.01 and 10
# Initialize arrays to store errors
training_errors = []
test_errors = []
sigma = 3
# Task 2b-d: Training, Testing, and Repeating the Experiment
# num_iterations = 100
for i in range(num_iterations):
# Generate data
X_train, y_train = data(100,sigma)
X_test, y_test = data(1000, sigma)
for lam in lambda_values:
# Train SVM
svm_problem_setup = svm_problem(y_train.tolist(), X_train.tolist())
param = svm_parameter(f'-t 0 -c {lam}')
model = svm_train(svm_problem_setup, param)
# Predict on training and test data
i, train_accuracy, i = svm_predict(y_train.tolist(), X_train.tolist(), model)
i, test_accuracy, i = svm_predict(y_test.tolist(), X_test.tolist(), model)
# Calculate errors
training_errors.append(100 - train_accuracy[0]) # Convert to error percentage
test_errors.append(100 - test_accuracy[0]) # Convert to error percentage
# Task 2e: Averaging Errors and Plotting
training_errors = np.array(training_errors).reshape(num_iterations, -1)
test_errors = np.array(test_errors).reshape(num_iterations, -1)
avg_training_error = np.mean(training_errors, axis=0)
avg_test_error = np.mean(test_errors, axis=0)
lambda_values_log = np.log10(lambda_values)
# Plotting
plt.figure(figsize=(10, 6))
plt.plot(lambda_values_log, avg_training_error, label='R_empirical (Average Training Error)')
plt.plot(lambda_values_log, avg_test_error, label='R_actual (Average Test Error)')
plt.plot(lambda_values_log, avg_test_error - avg_training_error, label='R_structural (Difference)')
plt.xlabel('log(λ)')
plt.ylabel('Error (%)')
plt.title('Risks vs. λ (0.1,10)@ σ = 3')
plt.legend()
plt.show()
* optimization finished, #iter = 38 nu = 0.190142 obj = -1.211850, rho = 0.037704 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 128 nu = 0.168626 obj = -1.335853, rho = 0.154344 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 146 nu = 0.142376 obj = -1.475279, rho = 0.175642 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 94 nu = 0.124389 obj = -1.630865, rho = 0.163166 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 72 nu = 0.108915 obj = -1.811548, rho = 0.154842 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 166 nu = 0.092236 obj = -2.015882, rho = 0.140347 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 98 nu = 0.085309 obj = -2.234035, rho = 0.262038 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .*.* optimization finished, #iter = 251 nu = 0.073788 obj = -2.423210, rho = 0.428452 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 95.8% (958/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.065300 obj = -2.596418, rho = 0.435621 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) *.* optimization finished, #iter = 186 nu = 0.054145 obj = -2.759100, rho = 0.433550 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) ..*.....* optimization finished, #iter = 769 nu = 0.044495 obj = -2.953266, rho = 0.434108 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.039693 obj = -3.140660, rho = 0.634223 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) .* optimization finished, #iter = 142 nu = 0.034888 obj = -3.197255, rho = 0.796246 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) .* optimization finished, #iter = 142 nu = 0.027379 obj = -3.197255, rho = 0.796246 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) .* optimization finished, #iter = 142 nu = 0.021486 obj = -3.197255, rho = 0.796246 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) .* optimization finished, #iter = 142 nu = 0.016861 obj = -3.197255, rho = 0.796246 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) .* optimization finished, #iter = 142 nu = 0.013232 obj = -3.197255, rho = 0.796246 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) .* optimization finished, #iter = 142 nu = 0.010384 obj = -3.197255, rho = 0.796246 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) .* optimization finished, #iter = 142 nu = 0.008149 obj = -3.197255, rho = 0.796246 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) .* optimization finished, #iter = 142 nu = 0.006395 obj = -3.197255, rho = 0.796246 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 55 nu = 0.175712 obj = -1.081133, rho = 0.335509 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 96 nu = 0.155307 obj = -1.173172, rho = 0.411492 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 39 nu = 0.133540 obj = -1.267691, rho = 0.437548 nSV = 16, nBSV = 11 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *..* optimization finished, #iter = 283 nu = 0.116267 obj = -1.336878, rho = 0.484385 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 187 nu = 0.095821 obj = -1.406824, rho = 0.487147 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*.* optimization finished, #iter = 236 nu = 0.080776 obj = -1.466724, rho = 0.484916 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 165 nu = 0.067614 obj = -1.503268, rho = 0.552078 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) .* optimization finished, #iter = 175 nu = 0.054624 obj = -1.528397, rho = 0.514669 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) ..* optimization finished, #iter = 251 nu = 0.044225 obj = -1.537308, rho = 0.469606 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) ..* optimization finished, #iter = 251 nu = 0.034706 obj = -1.537308, rho = 0.469606 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) ..* optimization finished, #iter = 251 nu = 0.027236 obj = -1.537308, rho = 0.469606 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) ..* optimization finished, #iter = 251 nu = 0.021373 obj = -1.537308, rho = 0.469606 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) ..* optimization finished, #iter = 251 nu = 0.016773 obj = -1.537308, rho = 0.469606 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) ..* optimization finished, #iter = 251 nu = 0.013163 obj = -1.537308, rho = 0.469606 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) ..* optimization finished, #iter = 251 nu = 0.010330 obj = -1.537308, rho = 0.469606 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) ..* optimization finished, #iter = 251 nu = 0.008106 obj = -1.537308, rho = 0.469606 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) ..* optimization finished, #iter = 251 nu = 0.006361 obj = -1.537308, rho = 0.469606 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) ..* optimization finished, #iter = 251 nu = 0.004992 obj = -1.537308, rho = 0.469606 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) ..* optimization finished, #iter = 251 nu = 0.003918 obj = -1.537308, rho = 0.469606 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) ..* optimization finished, #iter = 251 nu = 0.003074 obj = -1.537308, rho = 0.469606 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 40 nu = 0.173593 obj = -1.160380, rho = -0.165652 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 43 nu = 0.152592 obj = -1.315909, rho = -0.165012 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 46 nu = 0.139118 obj = -1.482378, rho = -0.066433 nSV = 17, nBSV = 12 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 82 nu = 0.122086 obj = -1.663047, rho = -0.055474 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 89 nu = 0.107640 obj = -1.866270, rho = -0.184768 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 174 nu = 0.094478 obj = -2.100472, rho = -0.138851 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 88 nu = 0.084317 obj = -2.360544, rho = -0.045985 nSV = 11, nBSV = 5 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 84 nu = 0.077725 obj = -2.618764, rho = 0.111645 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.068395 obj = -2.850873, rho = 0.211199 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.060196 obj = -3.063973, rho = 0.284546 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 251 nu = 0.053057 obj = -3.216588, rho = 0.353689 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) ..*.* optimization finished, #iter = 343 nu = 0.042487 obj = -3.338393, rho = 0.353893 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*....* optimization finished, #iter = 545 nu = 0.035146 obj = -3.466880, rho = 0.337154 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 138 nu = 0.029733 obj = -3.566059, rho = 0.287629 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*.* optimization finished, #iter = 201 nu = 0.023994 obj = -3.571136, rho = 0.254953 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 201 nu = 0.018830 obj = -3.571136, rho = 0.254953 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 201 nu = 0.014777 obj = -3.571136, rho = 0.254953 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 201 nu = 0.011596 obj = -3.571136, rho = 0.254953 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 201 nu = 0.009100 obj = -3.571136, rho = 0.254953 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 201 nu = 0.007141 obj = -3.571136, rho = 0.254953 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 92 nu = 0.249291 obj = -1.708948, rho = -0.180394 nSV = 29, nBSV = 21 Total nSV = 29 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 79 nu = 0.224763 obj = -1.944262, rho = -0.132052 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 87 nu = 0.204348 obj = -2.198305, rho = -0.147212 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.179477 obj = -2.473719, rho = -0.154872 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 178 nu = 0.159024 obj = -2.790455, rho = -0.235622 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 157 nu = 0.142783 obj = -3.146709, rho = -0.328538 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..*.* optimization finished, #iter = 336 nu = 0.123386 obj = -3.535789, rho = -0.361930 nSV = 18, nBSV = 7 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) ..*.* optimization finished, #iter = 309 nu = 0.107453 obj = -4.023285, rho = -0.365891 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..*.* optimization finished, #iter = 313 nu = 0.093839 obj = -4.611735, rho = -0.353565 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) ...*.* optimization finished, #iter = 487 nu = 0.082198 obj = -5.346888, rho = -0.352906 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.073965 obj = -6.276517, rho = -0.339983 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 127 nu = 0.069729 obj = -7.361163, rho = -0.309775 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.065347 obj = -8.532157, rho = -0.328318 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 185 nu = 0.059641 obj = -9.850531, rho = -0.371385 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*..* optimization finished, #iter = 350 nu = 0.055435 obj = -11.287150, rho = -0.351422 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 186 nu = 0.050805 obj = -12.833200, rho = -0.295450 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 147 nu = 0.048525 obj = -14.255105, rho = -0.265319 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..* optimization finished, #iter = 232 nu = 0.046739 obj = -15.101266, rho = -0.227109 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) ....*........* optimization finished, #iter = 1269 nu = 0.038617 obj = -15.153419, rho = -0.206516 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) ....*........* optimization finished, #iter = 1269 nu = 0.030305 obj = -15.153419, rho = -0.206516 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) * optimization finished, #iter = 60 nu = 0.187273 obj = -1.335359, rho = -0.014571 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 90 nu = 0.168301 obj = -1.547234, rho = -0.049823 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 70 nu = 0.151325 obj = -1.800956, rho = -0.072736 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.138608 obj = -2.102194, rho = -0.059102 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 78 nu = 0.128927 obj = -2.451340, rho = -0.066736 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 156 nu = 0.117136 obj = -2.833913, rho = -0.021689 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 98 nu = 0.104989 obj = -3.300741, rho = -0.035129 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 96.5% (965/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.096628 obj = -3.838569, rho = 0.023255 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 96% (960/1000) (classification) .* optimization finished, #iter = 144 nu = 0.089095 obj = -4.457789, rho = 0.075214 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.1% (961/1000) (classification) .* optimization finished, #iter = 179 nu = 0.078578 obj = -5.200102, rho = 0.052191 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.1% (961/1000) (classification) .*.* optimization finished, #iter = 224 nu = 0.070778 obj = -6.133355, rho = 0.007236 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.2% (962/1000) (classification) *.* optimization finished, #iter = 153 nu = 0.064047 obj = -7.298998, rho = 0.012960 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.062285 obj = -8.685966, rho = -0.022389 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) .* optimization finished, #iter = 169 nu = 0.057633 obj = -10.211676, rho = -0.045127 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.052225 obj = -12.101779, rho = -0.054145 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 72 nu = 0.048517 obj = -14.432139, rho = -0.041179 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 92 nu = 0.047922 obj = -17.047651, rho = 0.207391 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 94.9% (949/1000) (classification) .* optimization finished, #iter = 191 nu = 0.046952 obj = -19.623858, rho = 0.578223 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 94.3% (943/1000) (classification) .*.* optimization finished, #iter = 206 nu = 0.044472 obj = -22.024924, rho = 0.698877 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.5% (945/1000) (classification) ....*.* optimization finished, #iter = 574 nu = 0.041991 obj = -23.859942, rho = 0.782367 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.1% (941/1000) (classification) .* optimization finished, #iter = 130 nu = 0.171143 obj = -1.133502, rho = -0.180772 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.151269 obj = -1.278264, rho = -0.250186 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 242 nu = 0.131107 obj = -1.444885, rho = -0.268659 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) ..* optimization finished, #iter = 298 nu = 0.113946 obj = -1.651005, rho = -0.305198 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) ....*...* optimization finished, #iter = 739 nu = 0.099347 obj = -1.909732, rho = -0.305928 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) ....*...* optimization finished, #iter = 728 nu = 0.087980 obj = -2.237336, rho = -0.311764 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) ...* optimization finished, #iter = 375 nu = 0.079228 obj = -2.651778, rho = -0.337154 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*..* optimization finished, #iter = 311 nu = 0.072516 obj = -3.170408, rho = -0.371413 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 252 nu = 0.068240 obj = -3.805756, rho = -0.456679 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 234 nu = 0.063265 obj = -4.585348, rho = -0.466610 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..* optimization finished, #iter = 240 nu = 0.060539 obj = -5.527097, rho = -0.663144 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 221 nu = 0.055624 obj = -6.683090, rho = -0.632158 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..* optimization finished, #iter = 272 nu = 0.052137 obj = -8.149045, rho = -0.607920 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 152 nu = 0.050006 obj = -9.971047, rho = -0.671862 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 254 nu = 0.048993 obj = -12.149611, rho = -0.928772 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*..* optimization finished, #iter = 311 nu = 0.045701 obj = -14.791138, rho = -0.924287 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..*.* optimization finished, #iter = 387 nu = 0.043145 obj = -18.135940, rho = -0.863235 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..* optimization finished, #iter = 274 nu = 0.041118 obj = -22.345253, rho = -0.901442 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) ....*.* optimization finished, #iter = 505 nu = 0.039977 obj = -27.556118, rho = -1.188709 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) .....*..* optimization finished, #iter = 730 nu = 0.038023 obj = -34.041136, rho = -1.347543 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 92 nu = 0.212541 obj = -1.506523, rho = -0.124411 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 65 nu = 0.198440 obj = -1.730955, rho = -0.075045 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 65 nu = 0.183197 obj = -1.953831, rho = -0.058009 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 85 nu = 0.162013 obj = -2.197747, rho = -0.108115 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.147533 obj = -2.446874, rho = -0.163820 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 172 nu = 0.131375 obj = -2.677614, rho = -0.177173 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 271 nu = 0.109549 obj = -2.932315, rho = -0.170800 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*..*.* optimization finished, #iter = 430 nu = 0.094829 obj = -3.228279, rho = -0.173321 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) ...* optimization finished, #iter = 378 nu = 0.080223 obj = -3.567026, rho = -0.220145 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) ....* optimization finished, #iter = 460 nu = 0.068515 obj = -3.986497, rho = -0.254174 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) ..*.* optimization finished, #iter = 345 nu = 0.059955 obj = -4.491302, rho = -0.257723 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..* optimization finished, #iter = 255 nu = 0.056670 obj = -4.989096, rho = -0.239631 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) ...* optimization finished, #iter = 373 nu = 0.051682 obj = -5.365760, rho = -0.285070 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ......*...* optimization finished, #iter = 993 nu = 0.045348 obj = -5.584652, rho = -0.351764 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ...*..* optimization finished, #iter = 529 nu = 0.038183 obj = -5.682394, rho = -0.466340 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ...*..* optimization finished, #iter = 529 nu = 0.029964 obj = -5.682394, rho = -0.466340 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ...*..* optimization finished, #iter = 529 nu = 0.023515 obj = -5.682394, rho = -0.466340 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ...*..* optimization finished, #iter = 529 nu = 0.018453 obj = -5.682394, rho = -0.466340 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ...*..* optimization finished, #iter = 529 nu = 0.014482 obj = -5.682394, rho = -0.466340 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ...*..* optimization finished, #iter = 529 nu = 0.011365 obj = -5.682394, rho = -0.466340 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 29 nu = 0.194773 obj = -1.307570, rho = -0.300023 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 41 nu = 0.180788 obj = -1.465473, rho = -0.416844 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 60 nu = 0.163484 obj = -1.607930, rho = -0.540228 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.141186 obj = -1.746950, rho = -0.527930 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.121006 obj = -1.888524, rho = -0.572210 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 198 nu = 0.104818 obj = -2.024549, rho = -0.569011 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.090321 obj = -2.146171, rho = -0.596743 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..*..* optimization finished, #iter = 449 nu = 0.075557 obj = -2.240451, rho = -0.685346 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*...* optimization finished, #iter = 425 nu = 0.062652 obj = -2.314862, rho = -0.729307 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 211 nu = 0.051901 obj = -2.370978, rho = -0.772618 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 187 nu = 0.042156 obj = -2.379443, rho = -0.805414 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 187 nu = 0.033082 obj = -2.379443, rho = -0.805414 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 187 nu = 0.025962 obj = -2.379443, rho = -0.805414 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 187 nu = 0.020374 obj = -2.379443, rho = -0.805414 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 187 nu = 0.015989 obj = -2.379443, rho = -0.805414 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 187 nu = 0.012547 obj = -2.379443, rho = -0.805414 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 187 nu = 0.009847 obj = -2.379443, rho = -0.805414 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 187 nu = 0.007727 obj = -2.379443, rho = -0.805414 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 187 nu = 0.006064 obj = -2.379443, rho = -0.805414 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 187 nu = 0.004759 obj = -2.379443, rho = -0.805414 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 82 nu = 0.207519 obj = -1.335538, rho = 0.072515 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 156 nu = 0.186752 obj = -1.476775, rho = -0.004267 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 84 nu = 0.160300 obj = -1.622340, rho = -0.019065 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) ..* optimization finished, #iter = 291 nu = 0.137893 obj = -1.791734, rho = 0.043588 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 151 nu = 0.117336 obj = -1.983185, rho = 0.066554 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 68 nu = 0.104434 obj = -2.206914, rho = 0.111786 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 76 nu = 0.092299 obj = -2.417836, rho = 0.144021 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 164 nu = 0.077946 obj = -2.647609, rho = 0.150062 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 70 nu = 0.067338 obj = -2.922227, rho = 0.127842 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.057928 obj = -3.216176, rho = 0.087669 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 68 nu = 0.054167 obj = -3.490285, rho = -0.045292 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.049894 obj = -3.589146, rho = -0.213807 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.039155 obj = -3.589146, rho = -0.213807 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.030727 obj = -3.589146, rho = -0.213807 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.024113 obj = -3.589146, rho = -0.213807 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.018923 obj = -3.589146, rho = -0.213807 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.014850 obj = -3.589146, rho = -0.213807 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.011654 obj = -3.589146, rho = -0.213807 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.009145 obj = -3.589146, rho = -0.213807 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.007177 obj = -3.589146, rho = -0.213807 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.191079 obj = -1.245560, rho = -0.198563 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.168929 obj = -1.393213, rho = -0.302385 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 218 nu = 0.146788 obj = -1.558523, rho = -0.319941 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.130007 obj = -1.751253, rho = -0.334912 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 79 nu = 0.115025 obj = -1.957320, rho = -0.288714 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 98 nu = 0.108386 obj = -2.144655, rho = -0.187312 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..* optimization finished, #iter = 225 nu = 0.091893 obj = -2.294123, rho = -0.187909 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 138 nu = 0.076451 obj = -2.466057, rho = -0.282397 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 166 nu = 0.068513 obj = -2.606659, rho = -0.254493 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 187 nu = 0.059165 obj = -2.681879, rho = -0.279746 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *...*....* optimization finished, #iter = 645 nu = 0.047634 obj = -2.688727, rho = -0.334395 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *...*....* optimization finished, #iter = 645 nu = 0.037381 obj = -2.688727, rho = -0.334395 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *...*....* optimization finished, #iter = 645 nu = 0.029335 obj = -2.688727, rho = -0.334395 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *...*....* optimization finished, #iter = 645 nu = 0.023021 obj = -2.688727, rho = -0.334395 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *...*....* optimization finished, #iter = 645 nu = 0.018066 obj = -2.688727, rho = -0.334395 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *...*....* optimization finished, #iter = 645 nu = 0.014177 obj = -2.688727, rho = -0.334395 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *...*....* optimization finished, #iter = 645 nu = 0.011126 obj = -2.688727, rho = -0.334395 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *...*....* optimization finished, #iter = 645 nu = 0.008731 obj = -2.688727, rho = -0.334395 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *...*....* optimization finished, #iter = 645 nu = 0.006852 obj = -2.688727, rho = -0.334395 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *...*....* optimization finished, #iter = 645 nu = 0.005377 obj = -2.688727, rho = -0.334395 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 94 nu = 0.166215 obj = -1.163139, rho = -0.215178 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 167 nu = 0.149299 obj = -1.337200, rho = -0.250490 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 185 nu = 0.138110 obj = -1.530368, rho = -0.341846 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*.* optimization finished, #iter = 309 nu = 0.126574 obj = -1.729722, rho = -0.442154 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.112506 obj = -1.947217, rho = -0.422442 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.099144 obj = -2.185709, rho = -0.428443 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.091601 obj = -2.426897, rho = -0.457803 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 98 nu = 0.080720 obj = -2.634548, rho = -0.438827 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 50 nu = 0.073026 obj = -2.826894, rho = -0.416927 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.062897 obj = -2.902346, rho = -0.444817 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 234 nu = 0.051544 obj = -2.942502, rho = -0.326364 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ....*..* optimization finished, #iter = 698 nu = 0.041015 obj = -2.950461, rho = -0.301369 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ....*..* optimization finished, #iter = 698 nu = 0.032187 obj = -2.950461, rho = -0.301369 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ....*..* optimization finished, #iter = 698 nu = 0.025259 obj = -2.950461, rho = -0.301369 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ....*..* optimization finished, #iter = 698 nu = 0.019822 obj = -2.950461, rho = -0.301369 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ....*..* optimization finished, #iter = 698 nu = 0.015556 obj = -2.950461, rho = -0.301369 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ....*..* optimization finished, #iter = 698 nu = 0.012208 obj = -2.950461, rho = -0.301369 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ....*..* optimization finished, #iter = 698 nu = 0.009580 obj = -2.950461, rho = -0.301369 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ....*..* optimization finished, #iter = 698 nu = 0.007518 obj = -2.950461, rho = -0.301369 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ....*..* optimization finished, #iter = 698 nu = 0.005900 obj = -2.950461, rho = -0.301369 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 34 nu = 0.161561 obj = -0.986286, rho = -0.126367 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 52 nu = 0.137563 obj = -1.076321, rho = -0.144623 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 57 nu = 0.121571 obj = -1.175386, rho = -0.105322 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 92 nu = 0.103195 obj = -1.269569, rho = -0.087144 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.087726 obj = -1.368592, rho = -0.096076 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.073811 obj = -1.477829, rho = -0.128120 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 130 nu = 0.066616 obj = -1.575423, rho = -0.052239 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.054926 obj = -1.645826, rho = -0.147296 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.045690 obj = -1.713288, rho = -0.362688 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 97 nu = 0.038019 obj = -1.763860, rho = -0.489167 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 161 nu = 0.031079 obj = -1.792355, rho = -0.470779 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.024948 obj = -1.794305, rho = -0.480743 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.019578 obj = -1.794305, rho = -0.480743 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.015364 obj = -1.794305, rho = -0.480743 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.012057 obj = -1.794305, rho = -0.480743 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.009462 obj = -1.794305, rho = -0.480743 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.007425 obj = -1.794305, rho = -0.480743 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.005827 obj = -1.794305, rho = -0.480743 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.004573 obj = -1.794305, rho = -0.480743 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.003589 obj = -1.794305, rho = -0.480743 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 80 nu = 0.147228 obj = -0.856292, rho = -0.356050 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.123908 obj = -0.912789, rho = -0.348238 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.104587 obj = -0.974476, rho = -0.321407 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.086975 obj = -1.040742, rho = -0.308277 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 239 nu = 0.074591 obj = -1.107024, rho = -0.381683 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 285 nu = 0.062463 obj = -1.165801, rho = -0.334847 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *............* optimization finished, #iter = 1204 nu = 0.051069 obj = -1.223788, rho = -0.359506 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 171 nu = 0.042475 obj = -1.292433, rho = -0.394104 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 151 nu = 0.037140 obj = -1.336989, rho = -0.452875 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.030374 obj = -1.345409, rho = -0.425509 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.023836 obj = -1.345409, rho = -0.425509 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.018706 obj = -1.345409, rho = -0.425509 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.014680 obj = -1.345409, rho = -0.425509 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.011520 obj = -1.345409, rho = -0.425509 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.009040 obj = -1.345409, rho = -0.425509 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.007095 obj = -1.345409, rho = -0.425509 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.005567 obj = -1.345409, rho = -0.425509 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.004369 obj = -1.345409, rho = -0.425509 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.003429 obj = -1.345409, rho = -0.425509 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.002691 obj = -1.345409, rho = -0.425509 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 30 nu = 0.190048 obj = -1.288477, rho = -0.085866 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 41 nu = 0.173839 obj = -1.456828, rho = -0.107094 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *..* optimization finished, #iter = 203 nu = 0.153510 obj = -1.631474, rho = -0.120458 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*..* optimization finished, #iter = 344 nu = 0.131260 obj = -1.841658, rho = -0.129151 nSV = 19, nBSV = 8 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.116171 obj = -2.098659, rho = -0.114927 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*....* optimization finished, #iter = 571 nu = 0.102378 obj = -2.394181, rho = -0.092027 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 210 nu = 0.091046 obj = -2.754537, rho = -0.138335 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 159 nu = 0.081535 obj = -3.184606, rho = -0.155413 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 129 nu = 0.075639 obj = -3.669144, rho = -0.080161 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 175 nu = 0.069550 obj = -4.188517, rho = -0.059769 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 176 nu = 0.062562 obj = -4.742743, rho = 0.038742 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) .*.* optimization finished, #iter = 263 nu = 0.058216 obj = -5.312645, rho = 0.137833 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .*.* optimization finished, #iter = 274 nu = 0.051920 obj = -5.819678, rho = 0.188368 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) .*.* optimization finished, #iter = 239 nu = 0.043292 obj = -6.399958, rho = 0.166964 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) ..*.* optimization finished, #iter = 309 nu = 0.036522 obj = -7.134192, rho = 0.139888 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .* optimization finished, #iter = 155 nu = 0.031783 obj = -8.054805, rho = 0.074215 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.4% (954/1000) (classification) .* optimization finished, #iter = 174 nu = 0.030320 obj = -8.966570, rho = 0.101007 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.028915 obj = -9.551514, rho = 0.275146 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.1% (941/1000) (classification) ..* optimization finished, #iter = 243 nu = 0.024500 obj = -9.616108, rho = 0.340377 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 93.8% (938/1000) (classification) ..* optimization finished, #iter = 243 nu = 0.019227 obj = -9.616108, rho = 0.340377 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 93.8% (938/1000) (classification) * optimization finished, #iter = 35 nu = 0.141852 obj = -0.870762, rho = -0.184323 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 162 nu = 0.124128 obj = -0.950910, rho = -0.148700 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) *.* optimization finished, #iter = 169 nu = 0.107123 obj = -1.023211, rho = -0.119285 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.090942 obj = -1.098913, rho = -0.104812 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 96 nu = 0.077685 obj = -1.174442, rho = -0.119725 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 66 nu = 0.065659 obj = -1.243804, rho = -0.157453 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 88 nu = 0.056167 obj = -1.306402, rho = -0.186374 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 60 nu = 0.047438 obj = -1.339076, rho = -0.147674 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 66 nu = 0.038838 obj = -1.349808, rho = -0.105248 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 66 nu = 0.030479 obj = -1.349808, rho = -0.105248 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 66 nu = 0.023918 obj = -1.349808, rho = -0.105248 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 66 nu = 0.018770 obj = -1.349808, rho = -0.105248 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 66 nu = 0.014730 obj = -1.349808, rho = -0.105248 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 66 nu = 0.011560 obj = -1.349808, rho = -0.105248 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 66 nu = 0.009072 obj = -1.349808, rho = -0.105248 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 66 nu = 0.007119 obj = -1.349808, rho = -0.105248 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 66 nu = 0.005587 obj = -1.349808, rho = -0.105248 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 66 nu = 0.004384 obj = -1.349808, rho = -0.105248 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 66 nu = 0.003441 obj = -1.349808, rho = -0.105248 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 66 nu = 0.002700 obj = -1.349808, rho = -0.105248 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 99 nu = 0.187092 obj = -1.251778, rho = -0.108645 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 152 nu = 0.163463 obj = -1.418226, rho = -0.101612 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.142363 obj = -1.621378, rho = -0.100775 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 57 nu = 0.130505 obj = -1.857364, rho = -0.133374 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 69 nu = 0.123343 obj = -2.098444, rho = -0.111968 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.110597 obj = -2.324029, rho = -0.207886 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.093086 obj = -2.577955, rho = -0.216451 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 178 nu = 0.080244 obj = -2.892723, rho = -0.193482 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 210 nu = 0.069681 obj = -3.271024, rho = -0.206370 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.063583 obj = -3.694191, rho = -0.255487 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..*..* optimization finished, #iter = 468 nu = 0.057249 obj = -4.107344, rho = -0.309381 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*...* optimization finished, #iter = 500 nu = 0.052566 obj = -4.522704, rho = -0.410637 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*.* optimization finished, #iter = 276 nu = 0.046855 obj = -4.798405, rho = -0.505406 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 144 nu = 0.038917 obj = -5.059474, rho = -0.512196 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 133 nu = 0.034864 obj = -5.206533, rho = -0.523086 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 166 nu = 0.027452 obj = -5.206635, rho = -0.522594 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 166 nu = 0.021543 obj = -5.206635, rho = -0.522594 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 166 nu = 0.016906 obj = -5.206635, rho = -0.522594 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 166 nu = 0.013267 obj = -5.206635, rho = -0.522594 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 166 nu = 0.010412 obj = -5.206635, rho = -0.522594 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.213128 obj = -1.372455, rho = -0.078714 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.181497 obj = -1.537311, rho = -0.075637 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 150 nu = 0.159227 obj = -1.738441, rho = -0.083419 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.146997 obj = -1.947987, rho = 0.020370 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 88 nu = 0.126484 obj = -2.178518, rho = 0.025646 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 140 nu = 0.112620 obj = -2.428864, rho = 0.087043 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 205 nu = 0.099291 obj = -2.694429, rho = 0.102108 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.087665 obj = -2.972152, rho = 0.121113 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.074632 obj = -3.274518, rho = 0.049755 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.063575 obj = -3.640537, rho = 0.012910 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 226 nu = 0.055528 obj = -4.064832, rho = 0.110019 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 136 nu = 0.050496 obj = -4.510027, rho = 0.218887 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) ...* optimization finished, #iter = 390 nu = 0.044319 obj = -4.936676, rho = 0.089757 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) ...*...* optimization finished, #iter = 679 nu = 0.038609 obj = -5.355719, rho = -0.010010 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*......* optimization finished, #iter = 828 nu = 0.034118 obj = -5.726028, rho = -0.127838 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ....*...............* optimization finished, #iter = 1912 nu = 0.029068 obj = -5.976271, rho = -0.194124 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ....*.* optimization finished, #iter = 574 nu = 0.025157 obj = -6.080588, rho = -0.297027 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ....*.* optimization finished, #iter = 574 nu = 0.019742 obj = -6.080588, rho = -0.297027 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ....*.* optimization finished, #iter = 574 nu = 0.015493 obj = -6.080588, rho = -0.297027 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ....*.* optimization finished, #iter = 574 nu = 0.012158 obj = -6.080588, rho = -0.297027 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 33 nu = 0.205646 obj = -1.292887, rho = 0.053565 nSV = 23, nBSV = 19 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 172 nu = 0.181097 obj = -1.421300, rho = -0.040715 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.158970 obj = -1.542400, rho = -0.019891 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 76 nu = 0.134699 obj = -1.672872, rho = 0.042051 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.113174 obj = -1.816526, rho = 0.066126 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 92 nu = 0.097877 obj = -1.984528, rho = 0.075633 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 88 nu = 0.087727 obj = -2.131694, rho = 0.153758 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) .* optimization finished, #iter = 155 nu = 0.077228 obj = -2.215594, rho = 0.217039 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) ..*.* optimization finished, #iter = 355 nu = 0.063146 obj = -2.257552, rho = 0.236884 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.7% (947/1000) (classification) ..*..* optimization finished, #iter = 486 nu = 0.051429 obj = -2.279879, rho = 0.211487 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.5% (945/1000) (classification) ..*...* optimization finished, #iter = 522 nu = 0.040391 obj = -2.279891, rho = 0.210944 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.5% (945/1000) (classification) ..*...* optimization finished, #iter = 522 nu = 0.031698 obj = -2.279891, rho = 0.210944 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.5% (945/1000) (classification) ..*...* optimization finished, #iter = 522 nu = 0.024875 obj = -2.279891, rho = 0.210944 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.5% (945/1000) (classification) ..*...* optimization finished, #iter = 522 nu = 0.019521 obj = -2.279891, rho = 0.210944 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.5% (945/1000) (classification) ..*...* optimization finished, #iter = 522 nu = 0.015319 obj = -2.279891, rho = 0.210944 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.5% (945/1000) (classification) ..*...* optimization finished, #iter = 522 nu = 0.012022 obj = -2.279891, rho = 0.210944 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.5% (945/1000) (classification) ..*...* optimization finished, #iter = 522 nu = 0.009434 obj = -2.279891, rho = 0.210944 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.5% (945/1000) (classification) ..*...* optimization finished, #iter = 522 nu = 0.007404 obj = -2.279891, rho = 0.210944 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.5% (945/1000) (classification) ..*...* optimization finished, #iter = 522 nu = 0.005810 obj = -2.279891, rho = 0.210944 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.5% (945/1000) (classification) ..*...* optimization finished, #iter = 522 nu = 0.004560 obj = -2.279891, rho = 0.210944 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.5% (945/1000) (classification) * optimization finished, #iter = 54 nu = 0.182738 obj = -1.207963, rho = 0.063561 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 77 nu = 0.158948 obj = -1.365596, rho = -0.002421 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 42 nu = 0.143848 obj = -1.549144, rho = -0.032480 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 49 nu = 0.130104 obj = -1.736435, rho = -0.017447 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *..* optimization finished, #iter = 233 nu = 0.117866 obj = -1.918539, rho = 0.025467 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 98 nu = 0.099697 obj = -2.109960, rho = 0.024031 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 135 nu = 0.084797 obj = -2.341820, rho = 0.011620 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 229 nu = 0.074746 obj = -2.595588, rho = -0.038223 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 224 nu = 0.068062 obj = -2.847592, rho = 0.002933 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 197 nu = 0.060364 obj = -3.071030, rho = 0.003608 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) .* optimization finished, #iter = 166 nu = 0.054218 obj = -3.195589, rho = 0.054637 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .*.* optimization finished, #iter = 217 nu = 0.043524 obj = -3.254826, rho = 0.066491 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) ..* optimization finished, #iter = 298 nu = 0.035159 obj = -3.318704, rho = 0.048100 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.1% (951/1000) (classification) ...* optimization finished, #iter = 399 nu = 0.028466 obj = -3.324993, rho = 0.053285 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) ...* optimization finished, #iter = 399 nu = 0.022339 obj = -3.324993, rho = 0.053285 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) ...* optimization finished, #iter = 399 nu = 0.017531 obj = -3.324993, rho = 0.053285 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) ...* optimization finished, #iter = 399 nu = 0.013757 obj = -3.324993, rho = 0.053285 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) ...* optimization finished, #iter = 399 nu = 0.010796 obj = -3.324993, rho = 0.053285 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) ...* optimization finished, #iter = 399 nu = 0.008472 obj = -3.324993, rho = 0.053285 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) ...* optimization finished, #iter = 399 nu = 0.006649 obj = -3.324993, rho = 0.053285 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 51 nu = 0.190979 obj = -1.279977, rho = 0.047592 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.170492 obj = -1.442461, rho = 0.121770 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 89 nu = 0.152143 obj = -1.623934, rho = 0.254294 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.132586 obj = -1.824683, rho = 0.298439 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*............* optimization finished, #iter = 1348 nu = 0.116308 obj = -2.062203, rho = 0.300143 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.....*.....* optimization finished, #iter = 1004 nu = 0.101803 obj = -2.340182, rho = 0.294725 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.089391 obj = -2.680674, rho = 0.339040 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 97 nu = 0.078972 obj = -3.085722, rho = 0.370779 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 75 nu = 0.072952 obj = -3.566621, rho = 0.328045 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 181 nu = 0.068055 obj = -4.073507, rho = 0.322061 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.063779 obj = -4.573807, rho = 0.380656 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..*...* optimization finished, #iter = 583 nu = 0.056154 obj = -5.034069, rho = 0.399932 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*..* optimization finished, #iter = 382 nu = 0.047812 obj = -5.560090, rho = 0.389370 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.....* optimization finished, #iter = 541 nu = 0.040538 obj = -6.189986, rho = 0.386565 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.034566 obj = -6.985452, rho = 0.393188 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 203 nu = 0.029998 obj = -7.986415, rho = 0.426211 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 92 nu = 0.027474 obj = -9.199071, rho = 0.542817 nSV = 7, nBSV = 1 Total nSV = 7 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 151 nu = 0.026636 obj = -10.359203, rho = 0.798683 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 145 nu = 0.025832 obj = -11.174140, rho = 0.985516 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 192 nu = 0.022614 obj = -11.309655, rho = 1.098376 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 61 nu = 0.157613 obj = -1.030437, rho = 0.013231 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 50 nu = 0.139126 obj = -1.152899, rho = 0.080183 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.121451 obj = -1.295053, rho = 0.071719 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 59 nu = 0.107990 obj = -1.443918, rho = 0.041209 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 62 nu = 0.092591 obj = -1.617998, rho = 0.050458 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 78 nu = 0.086698 obj = -1.812691, rho = 0.080439 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 169 nu = 0.077554 obj = -1.970854, rho = 0.025790 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.066876 obj = -2.126189, rho = 0.056233 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 138 nu = 0.058235 obj = -2.257330, rho = 0.124605 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.050894 obj = -2.330579, rho = 0.185103 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.041464 obj = -2.340093, rho = 0.180949 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.032539 obj = -2.340093, rho = 0.180949 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.025535 obj = -2.340093, rho = 0.180949 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.020039 obj = -2.340093, rho = 0.180949 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.015726 obj = -2.340093, rho = 0.180949 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.012341 obj = -2.340093, rho = 0.180949 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.009685 obj = -2.340093, rho = 0.180949 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.007600 obj = -2.340093, rho = 0.180949 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.005964 obj = -2.340093, rho = 0.180949 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.004681 obj = -2.340093, rho = 0.180949 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.265896 obj = -1.858054, rho = 0.348981 nSV = 29, nBSV = 22 Total nSV = 29 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 54 nu = 0.238940 obj = -2.137121, rho = 0.373155 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 45 nu = 0.222324 obj = -2.438718, rho = 0.346080 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 88 nu = 0.195805 obj = -2.773556, rho = 0.416995 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 215 nu = 0.170197 obj = -3.177998, rho = 0.446785 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 143 nu = 0.151298 obj = -3.685576, rho = 0.407773 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 146 nu = 0.136719 obj = -4.290610, rho = 0.337033 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 284 nu = 0.124529 obj = -5.004673, rho = 0.300001 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) *..* optimization finished, #iter = 285 nu = 0.115154 obj = -5.828640, rho = 0.251511 nSV = 18, nBSV = 7 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 154 nu = 0.104564 obj = -6.807813, rho = 0.138383 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 173 nu = 0.095604 obj = -7.931233, rho = 0.013874 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 205 nu = 0.088583 obj = -9.244972, rho = -0.006153 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) .....* optimization finished, #iter = 580 nu = 0.081861 obj = -10.712890, rho = 0.017495 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 199 nu = 0.076405 obj = -12.327626, rho = 0.134934 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) ...*.* optimization finished, #iter = 451 nu = 0.072584 obj = -13.917517, rho = 0.361306 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..*.* optimization finished, #iter = 379 nu = 0.068058 obj = -15.286964, rho = 0.558594 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) ....*.* optimization finished, #iter = 503 nu = 0.056769 obj = -16.547065, rho = 0.336755 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .....*..* optimization finished, #iter = 735 nu = 0.047535 obj = -18.060565, rho = 0.332215 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .......*.* optimization finished, #iter = 815 nu = 0.042007 obj = -19.747230, rho = 0.879121 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ...*.* optimization finished, #iter = 498 nu = 0.035876 obj = -21.305092, rho = 1.140023 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 75 nu = 0.206496 obj = -1.418537, rho = -0.097582 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 51 nu = 0.187123 obj = -1.610201, rho = -0.040995 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 71 nu = 0.173174 obj = -1.819159, rho = 0.111910 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 119 nu = 0.155201 obj = -2.015981, rho = 0.222918 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 127 nu = 0.135126 obj = -2.230310, rho = 0.266953 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .**.* optimization finished, #iter = 218 nu = 0.122143 obj = -2.435942, rho = 0.425210 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ...*..* optimization finished, #iter = 545 nu = 0.109587 obj = -2.593240, rho = 0.458424 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 206 nu = 0.089837 obj = -2.721591, rho = 0.448631 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ...* optimization finished, #iter = 362 nu = 0.074937 obj = -2.855750, rho = 0.401120 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..*.* optimization finished, #iter = 360 nu = 0.062834 obj = -2.939556, rho = 0.332254 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*.* optimization finished, #iter = 304 nu = 0.050066 obj = -3.024865, rho = 0.332263 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 198 nu = 0.041273 obj = -3.114591, rho = 0.249701 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.034339 obj = -3.146816, rho = 0.145676 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.026948 obj = -3.146816, rho = 0.145676 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.021147 obj = -3.146816, rho = 0.145676 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.016596 obj = -3.146816, rho = 0.145676 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.013024 obj = -3.146816, rho = 0.145676 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.010220 obj = -3.146816, rho = 0.145676 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.008021 obj = -3.146816, rho = 0.145676 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.006294 obj = -3.146816, rho = 0.145676 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 134 nu = 0.144639 obj = -0.930977, rho = -0.106817 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 63 nu = 0.127240 obj = -1.036693, rho = -0.129136 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 71 nu = 0.109688 obj = -1.154433, rho = -0.127929 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 62 nu = 0.096665 obj = -1.287326, rho = -0.048861 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 54 nu = 0.082516 obj = -1.441400, rho = -0.025186 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 49 nu = 0.072005 obj = -1.629781, rho = -0.101271 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 64 nu = 0.064331 obj = -1.848723, rho = -0.118281 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 71 nu = 0.058641 obj = -2.075859, rho = -0.043141 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 40 nu = 0.052563 obj = -2.320680, rho = -0.116889 nSV = 8, nBSV = 2 Total nSV = 8 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 48 nu = 0.049478 obj = -2.528683, rho = -0.205478 nSV = 8, nBSV = 2 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.043147 obj = -2.659456, rho = -0.117514 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 88 nu = 0.037926 obj = -2.732419, rho = 0.058829 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 95 nu = 0.029811 obj = -2.732431, rho = 0.061598 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 95 nu = 0.023394 obj = -2.732431, rho = 0.061598 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 95 nu = 0.018359 obj = -2.732431, rho = 0.061598 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 95 nu = 0.014407 obj = -2.732431, rho = 0.061598 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 95 nu = 0.011306 obj = -2.732431, rho = 0.061598 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 95 nu = 0.008873 obj = -2.732431, rho = 0.061598 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 95 nu = 0.006963 obj = -2.732431, rho = 0.061598 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 95 nu = 0.005464 obj = -2.732431, rho = 0.061598 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 44 nu = 0.185158 obj = -1.208784, rho = -0.201174 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 58 nu = 0.162744 obj = -1.351634, rho = -0.230261 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 54 nu = 0.141571 obj = -1.516202, rho = -0.264220 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.128005 obj = -1.684907, rho = -0.206602 nSV = 18, nBSV = 7 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.110013 obj = -1.881045, rho = -0.225339 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *..* optimization finished, #iter = 206 nu = 0.097841 obj = -2.085160, rho = -0.274651 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *..* optimization finished, #iter = 256 nu = 0.084998 obj = -2.315500, rho = -0.384704 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 147 nu = 0.075060 obj = -2.562959, rho = -0.438756 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*.* optimization finished, #iter = 263 nu = 0.065689 obj = -2.816972, rho = -0.458526 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 199 nu = 0.056533 obj = -3.092039, rho = -0.517794 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) ..*....* optimization finished, #iter = 649 nu = 0.052063 obj = -3.332152, rho = -0.538881 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) ..*....* optimization finished, #iter = 632 nu = 0.047271 obj = -3.445211, rho = -0.529173 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) .*.....* optimization finished, #iter = 617 nu = 0.037609 obj = -3.446963, rho = -0.528889 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .*.....* optimization finished, #iter = 617 nu = 0.029514 obj = -3.446963, rho = -0.528889 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .*.....* optimization finished, #iter = 617 nu = 0.023162 obj = -3.446963, rho = -0.528889 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .*.....* optimization finished, #iter = 617 nu = 0.018176 obj = -3.446963, rho = -0.528889 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .*.....* optimization finished, #iter = 617 nu = 0.014264 obj = -3.446963, rho = -0.528889 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .*.....* optimization finished, #iter = 617 nu = 0.011194 obj = -3.446963, rho = -0.528889 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .*.....* optimization finished, #iter = 617 nu = 0.008784 obj = -3.446963, rho = -0.528889 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .*.....* optimization finished, #iter = 617 nu = 0.006894 obj = -3.446963, rho = -0.528889 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 61 nu = 0.202299 obj = -1.368657, rho = -0.367157 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.184226 obj = -1.542154, rho = -0.287227 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 78 nu = 0.158523 obj = -1.740773, rho = -0.289400 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 59 nu = 0.141967 obj = -1.974085, rho = -0.333608 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 54 nu = 0.124304 obj = -2.242747, rho = -0.429784 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 175 nu = 0.110533 obj = -2.561379, rho = -0.535067 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.102951 obj = -2.910738, rho = -0.660372 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 161 nu = 0.090315 obj = -3.280534, rho = -0.648941 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..*.* optimization finished, #iter = 351 nu = 0.080358 obj = -3.700041, rho = -0.588294 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*..* optimization finished, #iter = 317 nu = 0.073582 obj = -4.143386, rho = -0.537542 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) .*.* optimization finished, #iter = 258 nu = 0.063734 obj = -4.592292, rho = -0.434366 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*.* optimization finished, #iter = 324 nu = 0.054921 obj = -5.113157, rho = -0.469432 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 187 nu = 0.048089 obj = -5.716565, rho = -0.510025 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) .* optimization finished, #iter = 174 nu = 0.043354 obj = -6.359732, rho = -0.694257 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) *.* optimization finished, #iter = 157 nu = 0.037920 obj = -7.021942, rho = -0.956812 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.034966 obj = -7.611457, rho = -1.483408 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*.*..* optimization finished, #iter = 444 nu = 0.032436 obj = -7.878790, rho = -2.134473 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*...* optimization finished, #iter = 466 nu = 0.025584 obj = -7.879518, rho = -2.148372 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*...* optimization finished, #iter = 466 nu = 0.020077 obj = -7.879518, rho = -2.148372 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*...* optimization finished, #iter = 466 nu = 0.015756 obj = -7.879518, rho = -2.148372 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.170951 obj = -1.144064, rho = -0.201637 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 70 nu = 0.157965 obj = -1.284088, rho = -0.157845 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.139579 obj = -1.418153, rho = -0.259387 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 81 nu = 0.118861 obj = -1.566526, rho = -0.293171 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.102423 obj = -1.740821, rho = -0.300759 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.090017 obj = -1.947959, rho = -0.368392 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 61 nu = 0.079183 obj = -2.172712, rho = -0.385052 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.073538 obj = -2.382769, rho = -0.435996 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*...................* optimization finished, #iter = 2031 nu = 0.061343 obj = -2.578041, rho = -0.447246 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 171 nu = 0.053442 obj = -2.792799, rho = -0.370497 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 79 nu = 0.047330 obj = -2.975171, rho = -0.233760 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 97 nu = 0.042187 obj = -3.034315, rho = 0.029549 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 97 nu = 0.033107 obj = -3.034315, rho = 0.029549 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 97 nu = 0.025981 obj = -3.034315, rho = 0.029549 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 97 nu = 0.020389 obj = -3.034315, rho = 0.029549 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 97 nu = 0.016000 obj = -3.034315, rho = 0.029549 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 97 nu = 0.012556 obj = -3.034315, rho = 0.029549 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 97 nu = 0.009854 obj = -3.034315, rho = 0.029549 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 97 nu = 0.007733 obj = -3.034315, rho = 0.029549 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 97 nu = 0.006068 obj = -3.034315, rho = 0.029549 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 79 nu = 0.177757 obj = -1.124340, rho = -0.029778 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 72 nu = 0.152685 obj = -1.246996, rho = -0.000007 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 27 nu = 0.136909 obj = -1.378077, rho = -0.000905 nSV = 15, nBSV = 10 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 47 nu = 0.119817 obj = -1.507348, rho = -0.036055 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 83 nu = 0.101227 obj = -1.646574, rho = -0.097199 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.085232 obj = -1.812919, rho = -0.129056 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 157 nu = 0.073611 obj = -2.014099, rho = -0.232270 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 74 nu = 0.062600 obj = -2.250283, rho = -0.282380 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 40 nu = 0.054265 obj = -2.541886, rho = -0.284834 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 40 nu = 0.049820 obj = -2.873165, rho = -0.134933 nSV = 8, nBSV = 3 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 69 nu = 0.047639 obj = -3.156609, rho = 0.167778 nSV = 8, nBSV = 3 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 50 nu = 0.041941 obj = -3.343567, rho = 0.297445 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 51 nu = 0.037315 obj = -3.437442, rho = 0.613015 nSV = 7, nBSV = 1 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 60 nu = 0.029435 obj = -3.437614, rho = 0.627043 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 60 nu = 0.023100 obj = -3.437614, rho = 0.627043 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 60 nu = 0.018128 obj = -3.437614, rho = 0.627043 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 60 nu = 0.014226 obj = -3.437614, rho = 0.627043 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 60 nu = 0.011164 obj = -3.437614, rho = 0.627043 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 60 nu = 0.008761 obj = -3.437614, rho = 0.627043 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 60 nu = 0.006875 obj = -3.437614, rho = 0.627043 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.187180 obj = -1.308897, rho = -0.111540 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 88 nu = 0.165030 obj = -1.510017, rho = -0.092872 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 123 nu = 0.146777 obj = -1.757207, rho = -0.081217 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 69 nu = 0.133008 obj = -2.063493, rho = -0.021759 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 90 nu = 0.122587 obj = -2.426492, rho = 0.088450 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 87 nu = 0.115085 obj = -2.846318, rho = 0.165555 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 93 nu = 0.111134 obj = -3.292202, rho = 0.247798 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 95 nu = 0.105717 obj = -3.711939, rho = 0.313856 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.092466 obj = -4.140352, rho = 0.268185 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 171 nu = 0.081459 obj = -4.616607, rho = 0.292334 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.073647 obj = -5.098782, rho = 0.496566 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.064759 obj = -5.533499, rho = 0.601529 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 84 nu = 0.055323 obj = -5.953144, rho = 0.625522 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 89 nu = 0.049914 obj = -6.317886, rho = 0.513544 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.042972 obj = -6.396459, rho = 0.360584 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.033723 obj = -6.396459, rho = 0.360584 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.026464 obj = -6.396459, rho = 0.360584 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.020768 obj = -6.396459, rho = 0.360584 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.016298 obj = -6.396459, rho = 0.360584 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.012790 obj = -6.396459, rho = 0.360584 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 47 nu = 0.259025 obj = -1.923726, rho = -0.240595 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 46 nu = 0.240000 obj = -2.257793, rho = -0.175048 nSV = 27, nBSV = 22 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 92 nu = 0.217226 obj = -2.640954, rho = -0.252554 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 75 nu = 0.194701 obj = -3.120815, rho = -0.261417 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 58 nu = 0.180192 obj = -3.707247, rho = -0.338152 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.165853 obj = -4.423113, rho = -0.319247 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 294 nu = 0.153853 obj = -5.298374, rho = -0.210729 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 111 nu = 0.143493 obj = -6.384403, rho = -0.296067 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 135 nu = 0.136521 obj = -7.697058, rho = -0.439926 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*..*...* optimization finished, #iter = 688 nu = 0.128668 obj = -9.271240, rho = -0.549969 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 97% (97/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 196 nu = 0.123490 obj = -11.160634, rho = -0.734224 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 97% (97/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..* optimization finished, #iter = 220 nu = 0.117050 obj = -13.372124, rho = -0.845589 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 97% (97/100) (classification) Accuracy = 97% (970/1000) (classification) ..* optimization finished, #iter = 296 nu = 0.111752 obj = -15.965780, rho = -0.902239 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) ..* optimization finished, #iter = 274 nu = 0.108666 obj = -18.871582, rho = -0.954221 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) ..*.* optimization finished, #iter = 307 nu = 0.098810 obj = -22.158786, rho = -1.050094 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) ...* optimization finished, #iter = 367 nu = 0.094420 obj = -25.992840, rho = -1.011614 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 96.5% (965/1000) (classification) .......*..* optimization finished, #iter = 940 nu = 0.086835 obj = -30.010003, rho = -0.974993 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 96.5% (965/1000) (classification) ......*..* optimization finished, #iter = 828 nu = 0.076765 obj = -34.911326, rho = -1.023178 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 96.4% (964/1000) (classification) ........*.* optimization finished, #iter = 928 nu = 0.074413 obj = -40.483761, rho = -0.966420 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) ...*........* optimization finished, #iter = 1193 nu = 0.066079 obj = -46.246726, rho = -1.018957 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 56 nu = 0.201540 obj = -1.333375, rho = -0.253093 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 165 nu = 0.177109 obj = -1.498468, rho = -0.312877 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 53 nu = 0.157592 obj = -1.689499, rho = -0.293344 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 89 nu = 0.143648 obj = -1.887711, rho = -0.265597 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 82 nu = 0.125171 obj = -2.100820, rho = -0.260324 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *..* optimization finished, #iter = 234 nu = 0.110236 obj = -2.320305, rho = -0.259612 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 98 nu = 0.094701 obj = -2.569604, rho = -0.279543 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) ..* optimization finished, #iter = 219 nu = 0.084531 obj = -2.819245, rho = -0.288224 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..*.* optimization finished, #iter = 358 nu = 0.071450 obj = -3.094042, rho = -0.265629 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.061576 obj = -3.422083, rho = -0.272295 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 146 nu = 0.056262 obj = -3.730097, rho = -0.254693 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 147 nu = 0.049427 obj = -3.939988, rho = -0.225395 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 162 nu = 0.040527 obj = -4.136284, rho = -0.198393 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) ..* optimization finished, #iter = 293 nu = 0.033959 obj = -4.335952, rho = -0.244631 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ..* optimization finished, #iter = 269 nu = 0.029680 obj = -4.417737, rho = -0.206811 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..* optimization finished, #iter = 269 nu = 0.023292 obj = -4.417737, rho = -0.206811 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..* optimization finished, #iter = 269 nu = 0.018279 obj = -4.417737, rho = -0.206811 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..* optimization finished, #iter = 269 nu = 0.014344 obj = -4.417737, rho = -0.206811 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..* optimization finished, #iter = 269 nu = 0.011257 obj = -4.417737, rho = -0.206811 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..* optimization finished, #iter = 269 nu = 0.008834 obj = -4.417737, rho = -0.206811 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 69 nu = 0.189812 obj = -1.291869, rho = -0.047283 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 99 nu = 0.170018 obj = -1.466820, rho = -0.121602 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 140 nu = 0.151439 obj = -1.661388, rho = -0.119331 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 147 nu = 0.134873 obj = -1.886755, rho = -0.093918 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 160 nu = 0.123155 obj = -2.125312, rho = 0.081194 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 124 nu = 0.110976 obj = -2.371005, rho = 0.210797 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 148 nu = 0.094813 obj = -2.635635, rho = 0.213738 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*.* optimization finished, #iter = 261 nu = 0.081630 obj = -2.962974, rho = 0.204432 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 184 nu = 0.071665 obj = -3.355146, rho = 0.196498 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 123 nu = 0.065814 obj = -3.781781, rho = 0.349226 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96% (960/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.058719 obj = -4.195961, rho = 0.532937 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 96.1% (961/1000) (classification) ..*.* optimization finished, #iter = 375 nu = 0.052062 obj = -4.601216, rho = 0.596483 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 92 nu = 0.045139 obj = -5.044887, rho = 0.611493 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.2% (952/1000) (classification) ..*.* optimization finished, #iter = 308 nu = 0.040536 obj = -5.440196, rho = 0.681693 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) ..* optimization finished, #iter = 299 nu = 0.036738 obj = -5.681050, rho = 0.732456 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94% (940/1000) (classification) .....*.....* optimization finished, #iter = 1010 nu = 0.030043 obj = -5.696920, rho = 0.745301 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 93.7% (937/1000) (classification) .....*.....* optimization finished, #iter = 1010 nu = 0.023576 obj = -5.696920, rho = 0.745301 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 93.7% (937/1000) (classification) .....*.....* optimization finished, #iter = 1010 nu = 0.018502 obj = -5.696920, rho = 0.745301 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 93.7% (937/1000) (classification) .....*.....* optimization finished, #iter = 1010 nu = 0.014519 obj = -5.696920, rho = 0.745301 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 93.7% (937/1000) (classification) .....*.....* optimization finished, #iter = 1010 nu = 0.011394 obj = -5.696920, rho = 0.745301 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 93.7% (937/1000) (classification) * optimization finished, #iter = 95 nu = 0.179028 obj = -1.133844, rho = -0.190335 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 83 nu = 0.158849 obj = -1.254743, rho = -0.203205 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 176 nu = 0.139594 obj = -1.364865, rho = -0.237341 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 180 nu = 0.119381 obj = -1.476034, rho = -0.288844 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.099912 obj = -1.604888, rho = -0.277899 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.089944 obj = -1.726350, rho = -0.186606 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 91 nu = 0.076475 obj = -1.822605, rho = -0.155621 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 94 nu = 0.064088 obj = -1.901405, rho = -0.274333 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.052768 obj = -1.981678, rho = -0.140787 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*...* optimization finished, #iter = 406 nu = 0.043338 obj = -2.044428, rho = -0.051967 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..* optimization finished, #iter = 286 nu = 0.036629 obj = -2.090337, rho = 0.059436 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..*..* optimization finished, #iter = 404 nu = 0.029071 obj = -2.090832, rho = 0.076016 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*..* optimization finished, #iter = 404 nu = 0.022814 obj = -2.090832, rho = 0.076016 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*..* optimization finished, #iter = 404 nu = 0.017903 obj = -2.090832, rho = 0.076016 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*..* optimization finished, #iter = 404 nu = 0.014050 obj = -2.090832, rho = 0.076016 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*..* optimization finished, #iter = 404 nu = 0.011026 obj = -2.090832, rho = 0.076016 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*..* optimization finished, #iter = 404 nu = 0.008652 obj = -2.090832, rho = 0.076016 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*..* optimization finished, #iter = 404 nu = 0.006790 obj = -2.090832, rho = 0.076016 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*..* optimization finished, #iter = 404 nu = 0.005329 obj = -2.090832, rho = 0.076016 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*..* optimization finished, #iter = 404 nu = 0.004182 obj = -2.090832, rho = 0.076016 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 216 nu = 0.196818 obj = -1.243295, rho = -0.156813 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) .*..* optimization finished, #iter = 382 nu = 0.174127 obj = -1.373289, rho = -0.154057 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 91 nu = 0.153748 obj = -1.496393, rho = -0.188408 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 63 nu = 0.135007 obj = -1.616320, rho = -0.175324 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) .*..* optimization finished, #iter = 385 nu = 0.114441 obj = -1.712442, rho = -0.110124 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) .*..* optimization finished, #iter = 313 nu = 0.094573 obj = -1.815261, rho = -0.122221 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) ..*.* optimization finished, #iter = 316 nu = 0.079755 obj = -1.919241, rho = -0.139888 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) ..*.*.* optimization finished, #iter = 356 nu = 0.066097 obj = -2.028849, rho = -0.107411 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) ...*.* optimization finished, #iter = 481 nu = 0.054487 obj = -2.137150, rho = -0.091994 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) ..*.* optimization finished, #iter = 386 nu = 0.044268 obj = -2.270423, rho = -0.093317 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .*.* optimization finished, #iter = 211 nu = 0.037289 obj = -2.436337, rho = -0.069875 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*..*...* optimization finished, #iter = 581 nu = 0.034274 obj = -2.542085, rho = 0.058308 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) ..*....* optimization finished, #iter = 677 nu = 0.027779 obj = -2.546089, rho = 0.084205 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*....* optimization finished, #iter = 677 nu = 0.021800 obj = -2.546089, rho = 0.084205 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*....* optimization finished, #iter = 677 nu = 0.017108 obj = -2.546089, rho = 0.084205 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*....* optimization finished, #iter = 677 nu = 0.013426 obj = -2.546089, rho = 0.084205 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*....* optimization finished, #iter = 677 nu = 0.010536 obj = -2.546089, rho = 0.084205 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*....* optimization finished, #iter = 677 nu = 0.008268 obj = -2.546089, rho = 0.084205 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*....* optimization finished, #iter = 677 nu = 0.006488 obj = -2.546089, rho = 0.084205 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*....* optimization finished, #iter = 677 nu = 0.005092 obj = -2.546089, rho = 0.084205 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 169 nu = 0.167673 obj = -1.096399, rho = -0.062008 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 87 nu = 0.146857 obj = -1.233976, rho = -0.063880 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.132053 obj = -1.380754, rho = -0.076596 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 68 nu = 0.115768 obj = -1.539814, rho = -0.138593 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 87 nu = 0.104730 obj = -1.700696, rho = -0.148330 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.089381 obj = -1.859823, rho = -0.129682 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 186 nu = 0.078309 obj = -2.028354, rho = -0.200264 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 174 nu = 0.067432 obj = -2.210406, rho = -0.217823 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.056885 obj = -2.398802, rho = -0.221919 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 91 nu = 0.048456 obj = -2.615266, rho = -0.210478 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 150 nu = 0.044129 obj = -2.819171, rho = -0.020819 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ...* optimization finished, #iter = 399 nu = 0.039635 obj = -2.910633, rho = 0.169464 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.031788 obj = -2.913683, rho = 0.214012 nSV = 12, nBSV = 0 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.024946 obj = -2.913683, rho = 0.214012 nSV = 12, nBSV = 0 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.019577 obj = -2.913683, rho = 0.214012 nSV = 12, nBSV = 0 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.015363 obj = -2.913683, rho = 0.214012 nSV = 12, nBSV = 0 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.012056 obj = -2.913683, rho = 0.214012 nSV = 12, nBSV = 0 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.009461 obj = -2.913683, rho = 0.214012 nSV = 12, nBSV = 0 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.007425 obj = -2.913683, rho = 0.214012 nSV = 12, nBSV = 0 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.005827 obj = -2.913683, rho = 0.214012 nSV = 12, nBSV = 0 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 94 nu = 0.200008 obj = -1.311444, rho = -0.007047 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.176371 obj = -1.469096, rho = -0.001724 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.152438 obj = -1.654257, rho = -0.007924 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 63 nu = 0.140704 obj = -1.857413, rho = -0.027897 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 87 nu = 0.125174 obj = -2.049799, rho = 0.075005 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.111202 obj = -2.234269, rho = 0.264927 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.094094 obj = -2.417568, rho = 0.324036 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 95 nu = 0.079702 obj = -2.631691, rho = 0.352316 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 86 nu = 0.069600 obj = -2.840794, rho = 0.274121 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 226 nu = 0.060485 obj = -3.029322, rho = 0.204521 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*....* optimization finished, #iter = 538 nu = 0.050196 obj = -3.195304, rho = 0.253366 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 135 nu = 0.042650 obj = -3.363243, rho = 0.279933 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.035688 obj = -3.491874, rho = 0.337931 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) ...*....* optimization finished, #iter = 729 nu = 0.029981 obj = -3.556932, rho = 0.342575 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ...* optimization finished, #iter = 398 nu = 0.023936 obj = -3.562630, rho = 0.323115 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ...* optimization finished, #iter = 398 nu = 0.018784 obj = -3.562630, rho = 0.323115 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ...* optimization finished, #iter = 398 nu = 0.014741 obj = -3.562630, rho = 0.323115 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ...* optimization finished, #iter = 398 nu = 0.011568 obj = -3.562630, rho = 0.323115 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ...* optimization finished, #iter = 398 nu = 0.009078 obj = -3.562630, rho = 0.323115 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ...* optimization finished, #iter = 398 nu = 0.007124 obj = -3.562630, rho = 0.323115 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.194188 obj = -1.256703, rho = 0.130757 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.165472 obj = -1.410638, rho = 0.141211 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.150042 obj = -1.582703, rho = 0.069452 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.128229 obj = -1.779989, rho = 0.035343 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 91 nu = 0.110716 obj = -2.025153, rho = 0.044199 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.097974 obj = -2.325446, rho = 0.041188 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 85 nu = 0.086651 obj = -2.695904, rho = 0.040354 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 78 nu = 0.080435 obj = -3.130320, rho = 0.039306 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 66 nu = 0.078237 obj = -3.561752, rho = 0.261348 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 48 nu = 0.073996 obj = -3.936083, rho = 0.560746 nSV = 10, nBSV = 5 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.066475 obj = -4.174136, rho = 0.668398 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.054709 obj = -4.387322, rho = 0.608905 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.048092 obj = -4.577820, rho = 0.622514 nSV = 8, nBSV = 2 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.039277 obj = -4.586551, rho = 0.648518 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.030823 obj = -4.586551, rho = 0.648518 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.024189 obj = -4.586551, rho = 0.648518 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.018982 obj = -4.586551, rho = 0.648518 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.014896 obj = -4.586551, rho = 0.648518 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.011690 obj = -4.586551, rho = 0.648518 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.009174 obj = -4.586551, rho = 0.648518 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 169 nu = 0.184509 obj = -1.133065, rho = -0.179625 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 129 nu = 0.156793 obj = -1.241560, rho = -0.188246 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.134946 obj = -1.366567, rho = -0.225985 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..* optimization finished, #iter = 292 nu = 0.115801 obj = -1.507411, rho = -0.214040 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *...* optimization finished, #iter = 322 nu = 0.099005 obj = -1.666016, rho = -0.222984 nSV = 18, nBSV = 7 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.089246 obj = -1.844918, rho = -0.284142 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 66 nu = 0.080100 obj = -1.996424, rho = -0.385811 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 206 nu = 0.069857 obj = -2.107733, rho = -0.474065 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 182 nu = 0.057368 obj = -2.213616, rho = -0.531268 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 172 nu = 0.048785 obj = -2.313488, rho = -0.581383 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 171 nu = 0.039743 obj = -2.381016, rho = -0.614179 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 269 nu = 0.032596 obj = -2.438593, rho = -0.705531 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ..*..* optimization finished, #iter = 408 nu = 0.026795 obj = -2.455937, rho = -0.834601 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*..* optimization finished, #iter = 408 nu = 0.021028 obj = -2.455937, rho = -0.834601 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*..* optimization finished, #iter = 408 nu = 0.016502 obj = -2.455937, rho = -0.834601 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*..* optimization finished, #iter = 408 nu = 0.012950 obj = -2.455937, rho = -0.834601 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*..* optimization finished, #iter = 408 nu = 0.010162 obj = -2.455937, rho = -0.834601 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*..* optimization finished, #iter = 408 nu = 0.007975 obj = -2.455937, rho = -0.834601 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*..* optimization finished, #iter = 408 nu = 0.006259 obj = -2.455937, rho = -0.834601 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*..* optimization finished, #iter = 408 nu = 0.004911 obj = -2.455937, rho = -0.834601 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 161 nu = 0.199055 obj = -1.289171, rho = -0.173920 nSV = 26, nBSV = 15 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 74 nu = 0.172795 obj = -1.446635, rho = -0.188986 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 186 nu = 0.156053 obj = -1.614597, rho = -0.277435 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 261 nu = 0.137983 obj = -1.779019, rho = -0.360483 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.119910 obj = -1.961377, rho = -0.433046 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 248 nu = 0.105456 obj = -2.133572, rho = -0.534616 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 83 nu = 0.090942 obj = -2.311468, rho = -0.567242 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) *.* optimization finished, #iter = 166 nu = 0.079576 obj = -2.474900, rho = -0.472310 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) .* optimization finished, #iter = 145 nu = 0.068366 obj = -2.605195, rho = -0.601808 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 156 nu = 0.055987 obj = -2.711882, rho = -0.637456 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) ..*....* optimization finished, #iter = 638 nu = 0.046811 obj = -2.809636, rho = -0.729559 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) ...*.* optimization finished, #iter = 424 nu = 0.038010 obj = -2.895614, rho = -0.745039 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..* optimization finished, #iter = 296 nu = 0.031874 obj = -2.921663, rho = -0.865738 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) ..* optimization finished, #iter = 296 nu = 0.025014 obj = -2.921663, rho = -0.865738 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) ..* optimization finished, #iter = 296 nu = 0.019630 obj = -2.921663, rho = -0.865738 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) ..* optimization finished, #iter = 296 nu = 0.015405 obj = -2.921663, rho = -0.865738 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) ..* optimization finished, #iter = 296 nu = 0.012089 obj = -2.921663, rho = -0.865738 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) ..* optimization finished, #iter = 296 nu = 0.009487 obj = -2.921663, rho = -0.865738 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) ..* optimization finished, #iter = 296 nu = 0.007445 obj = -2.921663, rho = -0.865738 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) ..* optimization finished, #iter = 296 nu = 0.005843 obj = -2.921663, rho = -0.865738 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 95 nu = 0.185665 obj = -1.294277, rho = -0.124071 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 46 nu = 0.166709 obj = -1.489109, rho = -0.121377 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 99 nu = 0.147307 obj = -1.717388, rho = -0.102140 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 30 nu = 0.139842 obj = -1.978062, rho = 0.048685 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 84 nu = 0.123123 obj = -2.255682, rho = 0.013128 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 72 nu = 0.111392 obj = -2.585527, rho = -0.242179 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) .* optimization finished, #iter = 144 nu = 0.097757 obj = -2.965699, rho = -0.317175 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 136 nu = 0.089371 obj = -3.430851, rho = -0.576309 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 179 nu = 0.078802 obj = -3.963736, rho = -0.660991 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 143 nu = 0.071066 obj = -4.616644, rho = -0.742389 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.065306 obj = -5.388426, rho = -0.847764 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 162 nu = 0.059898 obj = -6.284779, rho = -0.924476 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 171 nu = 0.055303 obj = -7.289416, rho = -0.998818 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 196 nu = 0.050361 obj = -8.452774, rho = -0.965192 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 139 nu = 0.045398 obj = -9.839308, rho = -0.936558 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 134 nu = 0.042448 obj = -11.407981, rho = -0.878583 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 155 nu = 0.040133 obj = -13.050947, rho = -0.804972 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 161 nu = 0.038316 obj = -14.566547, rho = -0.711034 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 185 nu = 0.036890 obj = -15.559308, rho = -0.591063 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*.* optimization finished, #iter = 310 nu = 0.031317 obj = -15.662947, rho = -0.538236 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.204214 obj = -1.475932, rho = -0.086814 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 43 nu = 0.185667 obj = -1.719884, rho = -0.175495 nSV = 21, nBSV = 17 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 54 nu = 0.169082 obj = -1.999683, rho = -0.224224 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 97% (97/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 79 nu = 0.152648 obj = -2.333849, rho = -0.312941 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.139297 obj = -2.731271, rho = -0.370536 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 89 nu = 0.126481 obj = -3.214957, rho = -0.379801 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 62 nu = 0.120046 obj = -3.775758, rho = -0.284526 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 81 nu = 0.113422 obj = -4.385935, rho = -0.075149 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 151 nu = 0.102054 obj = -5.060978, rho = 0.058655 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 141 nu = 0.092339 obj = -5.859695, rho = 0.149142 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..* optimization finished, #iter = 238 nu = 0.084986 obj = -6.760269, rho = 0.207962 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 277 nu = 0.075820 obj = -7.821896, rho = 0.191518 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 275 nu = 0.070961 obj = -9.011351, rho = 0.154745 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) ..*.* optimization finished, #iter = 319 nu = 0.062790 obj = -10.302789, rho = 0.142527 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) ..*.* optimization finished, #iter = 301 nu = 0.054750 obj = -11.931262, rho = 0.143140 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 272 nu = 0.048440 obj = -14.006028, rho = 0.143620 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 85 nu = 0.044791 obj = -16.597927, rho = 0.038270 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.043124 obj = -19.537616, rho = -0.236183 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 168 nu = 0.040753 obj = -22.690250, rho = -0.493574 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 125 nu = 0.038880 obj = -25.932862, rho = -0.792891 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 69 nu = 0.217219 obj = -1.500098, rho = -0.130332 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 62 nu = 0.199538 obj = -1.706880, rho = -0.033961 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 78 nu = 0.176047 obj = -1.932872, rho = -0.018435 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 146 nu = 0.154854 obj = -2.199867, rho = -0.014190 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 86 nu = 0.141180 obj = -2.501188, rho = -0.031241 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.126485 obj = -2.818591, rho = -0.059786 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 173 nu = 0.114428 obj = -3.159973, rho = -0.224444 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*...* optimization finished, #iter = 410 nu = 0.099545 obj = -3.518628, rho = -0.274400 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.......* optimization finished, #iter = 855 nu = 0.085907 obj = -3.949155, rho = -0.275538 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*...........* optimization finished, #iter = 1230 nu = 0.075182 obj = -4.462437, rho = -0.253609 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*..* optimization finished, #iter = 331 nu = 0.065417 obj = -5.061671, rho = -0.218056 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 186 nu = 0.060438 obj = -5.744484, rho = 0.001775 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 226 nu = 0.053994 obj = -6.423684, rho = 0.146221 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 270 nu = 0.046676 obj = -7.186852, rho = 0.243234 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..* optimization finished, #iter = 262 nu = 0.040424 obj = -8.131474, rho = 0.166037 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .....* optimization finished, #iter = 556 nu = 0.037077 obj = -9.178807, rho = -0.033888 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 151 nu = 0.032929 obj = -10.287032, rho = -0.084789 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 157 nu = 0.031365 obj = -11.253205, rho = -0.365365 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ...* optimization finished, #iter = 359 nu = 0.029677 obj = -11.648572, rho = -0.723102 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ...* optimization finished, #iter = 359 nu = 0.023289 obj = -11.648572, rho = -0.723102 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.205868 obj = -1.380302, rho = -0.208858 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 92 nu = 0.178661 obj = -1.570581, rho = -0.158382 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.158127 obj = -1.797574, rho = -0.115892 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 68 nu = 0.142958 obj = -2.061977, rho = -0.172247 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 57 nu = 0.130621 obj = -2.366678, rho = -0.092628 nSV = 16, nBSV = 11 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 56 nu = 0.122421 obj = -2.676141, rho = -0.236231 nSV = 15, nBSV = 10 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .* optimization finished, #iter = 135 nu = 0.111512 obj = -2.954139, rho = -0.606016 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 64 nu = 0.099685 obj = -3.230118, rho = -0.776055 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 86 nu = 0.088884 obj = -3.435153, rho = -1.203720 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) .*.* optimization finished, #iter = 271 nu = 0.073696 obj = -3.587784, rho = -1.325669 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) .*.* optimization finished, #iter = 272 nu = 0.062304 obj = -3.740282, rho = -1.554980 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) ..*.* optimization finished, #iter = 327 nu = 0.049944 obj = -3.861257, rho = -1.571427 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) ..*.* optimization finished, #iter = 363 nu = 0.040768 obj = -3.990086, rho = -1.592109 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) ..*.* optimization finished, #iter = 328 nu = 0.034656 obj = -4.048293, rho = -1.720852 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) ..*.* optimization finished, #iter = 328 nu = 0.027197 obj = -4.048293, rho = -1.720852 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) ..*.* optimization finished, #iter = 328 nu = 0.021343 obj = -4.048293, rho = -1.720852 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) ..*.* optimization finished, #iter = 328 nu = 0.016749 obj = -4.048293, rho = -1.720852 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) ..*.* optimization finished, #iter = 328 nu = 0.013144 obj = -4.048293, rho = -1.720852 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) ..*.* optimization finished, #iter = 328 nu = 0.010315 obj = -4.048293, rho = -1.720852 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) ..*.* optimization finished, #iter = 328 nu = 0.008095 obj = -4.048293, rho = -1.720852 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 41 nu = 0.232775 obj = -1.677063, rho = -0.073471 nSV = 26, nBSV = 21 Total nSV = 26 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 83 nu = 0.220268 obj = -1.931773, rho = 0.085235 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 62 nu = 0.201313 obj = -2.193563, rho = 0.154833 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 168 nu = 0.178461 obj = -2.485855, rho = 0.142175 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *..* optimization finished, #iter = 210 nu = 0.156785 obj = -2.828511, rho = 0.135020 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 230 nu = 0.138439 obj = -3.225063, rho = 0.129293 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 149 nu = 0.120554 obj = -3.718082, rho = 0.128277 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 46 nu = 0.113225 obj = -4.297947, rho = 0.130502 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.107518 obj = -4.858702, rho = 0.121891 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 232 nu = 0.091695 obj = -5.464105, rho = 0.118820 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..* optimization finished, #iter = 281 nu = 0.080412 obj = -6.212168, rho = 0.192863 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) ...*...* optimization finished, #iter = 688 nu = 0.072685 obj = -7.083359, rho = 0.292608 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) ..*.* optimization finished, #iter = 341 nu = 0.066952 obj = -7.956223, rho = 0.301964 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) ..*.* optimization finished, #iter = 364 nu = 0.061488 obj = -8.799629, rho = 0.386571 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..*.* optimization finished, #iter = 372 nu = 0.051857 obj = -9.661883, rho = 0.530668 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .*.* optimization finished, #iter = 234 nu = 0.044681 obj = -10.673742, rho = 0.716700 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*.* optimization finished, #iter = 234 nu = 0.039206 obj = -11.771043, rho = 0.955080 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 246 nu = 0.033872 obj = -12.897537, rho = 1.029718 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 156 nu = 0.031700 obj = -13.909768, rho = 1.267614 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ..*.* optimization finished, #iter = 365 nu = 0.028340 obj = -14.174056, rho = 1.486013 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 61 nu = 0.188959 obj = -1.193870, rho = -0.079415 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 49 nu = 0.165225 obj = -1.319649, rho = -0.101348 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 76 nu = 0.147766 obj = -1.440437, rho = -0.166914 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.124998 obj = -1.566541, rho = -0.123229 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.111296 obj = -1.695997, rho = -0.141152 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *...* optimization finished, #iter = 305 nu = 0.098647 obj = -1.774865, rho = -0.162576 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 233 nu = 0.080489 obj = -1.832537, rho = -0.217610 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*.* optimization finished, #iter = 221 nu = 0.067072 obj = -1.864443, rho = -0.268997 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 259 nu = 0.053612 obj = -1.872868, rho = -0.288098 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ....* optimization finished, #iter = 481 nu = 0.042233 obj = -1.877982, rho = -0.290709 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ....*..* optimization finished, #iter = 658 nu = 0.033271 obj = -1.878367, rho = -0.295205 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ....*..* optimization finished, #iter = 658 nu = 0.026110 obj = -1.878367, rho = -0.295205 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ....*..* optimization finished, #iter = 658 nu = 0.020490 obj = -1.878367, rho = -0.295205 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ....*..* optimization finished, #iter = 658 nu = 0.016080 obj = -1.878367, rho = -0.295205 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ....*..* optimization finished, #iter = 658 nu = 0.012619 obj = -1.878367, rho = -0.295205 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ....*..* optimization finished, #iter = 658 nu = 0.009903 obj = -1.878367, rho = -0.295205 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ....*..* optimization finished, #iter = 658 nu = 0.007771 obj = -1.878367, rho = -0.295205 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ....*..* optimization finished, #iter = 658 nu = 0.006099 obj = -1.878367, rho = -0.295205 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ....*..* optimization finished, #iter = 658 nu = 0.004786 obj = -1.878367, rho = -0.295205 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ....*..* optimization finished, #iter = 658 nu = 0.003756 obj = -1.878367, rho = -0.295205 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 50 nu = 0.187982 obj = -1.225878, rho = -0.276289 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.165724 obj = -1.368166, rho = -0.284587 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.147769 obj = -1.523382, rho = -0.440394 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 47 nu = 0.132289 obj = -1.684869, rho = -0.569146 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 265 nu = 0.115478 obj = -1.826427, rho = -0.636730 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 169 nu = 0.099721 obj = -1.981204, rho = -0.718680 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*..* optimization finished, #iter = 434 nu = 0.086563 obj = -2.113821, rho = -0.784544 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.070693 obj = -2.256085, rho = -0.785817 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.059550 obj = -2.425366, rho = -0.780518 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) ....*.* optimization finished, #iter = 596 nu = 0.049589 obj = -2.612817, rho = -0.779541 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*.* optimization finished, #iter = 266 nu = 0.043249 obj = -2.806896, rho = -0.836879 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) .* optimization finished, #iter = 175 nu = 0.037999 obj = -2.949285, rho = -1.021316 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.032607 obj = -2.988258, rho = -1.130278 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.025589 obj = -2.988258, rho = -1.130278 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.020081 obj = -2.988258, rho = -1.130278 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.015759 obj = -2.988258, rho = -1.130278 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.012367 obj = -2.988258, rho = -1.130278 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.009705 obj = -2.988258, rho = -1.130278 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.007616 obj = -2.988258, rho = -1.130278 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.005977 obj = -2.988258, rho = -1.130278 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 57 nu = 0.190436 obj = -1.217605, rho = -0.130671 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 56 nu = 0.165375 obj = -1.356325, rho = -0.110647 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 150 nu = 0.144811 obj = -1.509052, rho = -0.154883 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.124923 obj = -1.685648, rho = -0.196026 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 72 nu = 0.111884 obj = -1.884804, rho = -0.313604 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 87 nu = 0.104982 obj = -2.058636, rho = -0.444681 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 127 nu = 0.094104 obj = -2.162630, rho = -0.570495 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 238 nu = 0.079476 obj = -2.202242, rho = -0.599301 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*..* optimization finished, #iter = 326 nu = 0.062765 obj = -2.218473, rho = -0.591029 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ...*...* optimization finished, #iter = 628 nu = 0.050272 obj = -2.227020, rho = -0.570156 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ...*...* optimization finished, #iter = 688 nu = 0.039454 obj = -2.227021, rho = -0.570051 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ...*...* optimization finished, #iter = 688 nu = 0.030962 obj = -2.227021, rho = -0.570051 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ...*...* optimization finished, #iter = 688 nu = 0.024298 obj = -2.227021, rho = -0.570051 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ...*...* optimization finished, #iter = 688 nu = 0.019068 obj = -2.227021, rho = -0.570051 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ...*...* optimization finished, #iter = 688 nu = 0.014964 obj = -2.227021, rho = -0.570051 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ...*...* optimization finished, #iter = 688 nu = 0.011743 obj = -2.227021, rho = -0.570051 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ...*...* optimization finished, #iter = 688 nu = 0.009215 obj = -2.227021, rho = -0.570051 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ...*...* optimization finished, #iter = 688 nu = 0.007232 obj = -2.227021, rho = -0.570051 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ...*...* optimization finished, #iter = 688 nu = 0.005675 obj = -2.227021, rho = -0.570051 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ...*...* optimization finished, #iter = 688 nu = 0.004454 obj = -2.227021, rho = -0.570051 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 84 nu = 0.232455 obj = -1.726107, rho = -0.101931 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 45 nu = 0.220000 obj = -2.020321, rho = -0.171945 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 96% (96/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 55 nu = 0.196055 obj = -2.353194, rho = -0.121493 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 96% (96/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 73 nu = 0.179525 obj = -2.762594, rho = -0.139451 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 96% (96/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 64 nu = 0.169578 obj = -3.226000, rho = -0.259009 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 96% (96/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 82 nu = 0.159803 obj = -3.711960, rho = -0.323006 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) *..* optimization finished, #iter = 221 nu = 0.142705 obj = -4.232789, rho = -0.368676 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) *..* optimization finished, #iter = 237 nu = 0.125668 obj = -4.866296, rho = -0.410660 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.117370 obj = -5.598272, rho = -0.329258 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) ...**.* optimization finished, #iter = 347 nu = 0.112397 obj = -6.292315, rho = -0.505826 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 96% (960/1000) (classification) .*.* optimization finished, #iter = 268 nu = 0.095659 obj = -6.978166, rho = -0.544633 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 95.8% (958/1000) (classification) ...* optimization finished, #iter = 326 nu = 0.085167 obj = -7.717598, rho = -0.653104 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 95% (950/1000) (classification) ...* optimization finished, #iter = 333 nu = 0.076788 obj = -8.465820, rho = -0.780726 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 94.8% (948/1000) (classification) ....*..* optimization finished, #iter = 655 nu = 0.065080 obj = -9.177351, rho = -0.764683 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 94.6% (946/1000) (classification) .* optimization finished, #iter = 155 nu = 0.055491 obj = -10.026137, rho = -0.776620 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 94.7% (947/1000) (classification) ..* optimization finished, #iter = 234 nu = 0.052412 obj = -10.674826, rho = -0.795177 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.5% (945/1000) (classification) ..*.* optimization finished, #iter = 372 nu = 0.044637 obj = -10.786275, rho = -0.846572 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.4% (944/1000) (classification) ..*.* optimization finished, #iter = 372 nu = 0.035029 obj = -10.786275, rho = -0.846572 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.4% (944/1000) (classification) ..*.* optimization finished, #iter = 372 nu = 0.027490 obj = -10.786275, rho = -0.846572 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.4% (944/1000) (classification) ..*.* optimization finished, #iter = 372 nu = 0.021573 obj = -10.786275, rho = -0.846572 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.4% (944/1000) (classification) * optimization finished, #iter = 78 nu = 0.158315 obj = -0.963541, rho = -0.054701 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 58 nu = 0.135594 obj = -1.049590, rho = -0.005342 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 61 nu = 0.117150 obj = -1.140994, rho = -0.039005 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 86 nu = 0.102740 obj = -1.228826, rho = 0.080660 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *........* optimization finished, #iter = 808 nu = 0.086795 obj = -1.310431, rho = 0.131343 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *........* optimization finished, #iter = 890 nu = 0.072509 obj = -1.388447, rho = 0.119435 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.*.* optimization finished, #iter = 299 nu = 0.059820 obj = -1.477513, rho = 0.085129 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 290 nu = 0.050328 obj = -1.575070, rho = 0.090123 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*..* optimization finished, #iter = 345 nu = 0.042379 obj = -1.673794, rho = 0.049111 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 167 nu = 0.036665 obj = -1.758800, rho = -0.016189 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 85 nu = 0.030994 obj = -1.811840, rho = -0.018953 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 145 nu = 0.025281 obj = -1.818610, rho = -0.019004 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 145 nu = 0.019840 obj = -1.818610, rho = -0.019004 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 145 nu = 0.015570 obj = -1.818610, rho = -0.019004 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 145 nu = 0.012218 obj = -1.818610, rho = -0.019004 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 145 nu = 0.009588 obj = -1.818610, rho = -0.019004 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 145 nu = 0.007525 obj = -1.818610, rho = -0.019004 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 145 nu = 0.005905 obj = -1.818610, rho = -0.019004 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 145 nu = 0.004634 obj = -1.818610, rho = -0.019004 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 145 nu = 0.003637 obj = -1.818610, rho = -0.019004 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 62 nu = 0.205412 obj = -1.351526, rho = -0.270914 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 127 nu = 0.176811 obj = -1.525623, rho = -0.281517 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 88 nu = 0.158063 obj = -1.730829, rho = -0.317283 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.143324 obj = -1.958193, rho = -0.362686 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.130992 obj = -2.185939, rho = -0.378315 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .*....* optimization finished, #iter = 566 nu = 0.118014 obj = -2.391665, rho = -0.407751 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) ..*.* optimization finished, #iter = 317 nu = 0.100885 obj = -2.599763, rho = -0.445996 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 145 nu = 0.089360 obj = -2.797711, rho = -0.501763 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 161 nu = 0.076547 obj = -2.967687, rho = -0.537541 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) .*.* optimization finished, #iter = 212 nu = 0.065458 obj = -3.076814, rho = -0.659372 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) .* optimization finished, #iter = 167 nu = 0.053275 obj = -3.158132, rho = -0.715137 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.043403 obj = -3.235726, rho = -0.715775 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) .* optimization finished, #iter = 158 nu = 0.035394 obj = -3.244133, rho = -0.754808 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .* optimization finished, #iter = 158 nu = 0.027776 obj = -3.244133, rho = -0.754808 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .* optimization finished, #iter = 158 nu = 0.021797 obj = -3.244133, rho = -0.754808 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .* optimization finished, #iter = 158 nu = 0.017106 obj = -3.244133, rho = -0.754808 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .* optimization finished, #iter = 158 nu = 0.013424 obj = -3.244133, rho = -0.754808 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .* optimization finished, #iter = 158 nu = 0.010534 obj = -3.244133, rho = -0.754808 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .* optimization finished, #iter = 158 nu = 0.008267 obj = -3.244133, rho = -0.754808 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .* optimization finished, #iter = 158 nu = 0.006488 obj = -3.244133, rho = -0.754808 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.237263 obj = -1.619325, rho = -0.502843 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 156 nu = 0.210298 obj = -1.844180, rho = -0.511575 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.189104 obj = -2.111402, rho = -0.585308 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 218 nu = 0.165028 obj = -2.420597, rho = -0.623357 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *..* optimization finished, #iter = 204 nu = 0.144505 obj = -2.811194, rho = -0.618937 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 142 nu = 0.131670 obj = -3.292492, rho = -0.715294 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 99 nu = 0.123046 obj = -3.839223, rho = -0.930158 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 120 nu = 0.114459 obj = -4.458733, rho = -0.955806 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*..* optimization finished, #iter = 311 nu = 0.103240 obj = -5.158229, rho = -0.936365 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 252 nu = 0.092555 obj = -6.010894, rho = -1.012196 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 289 nu = 0.083155 obj = -7.034739, rho = -1.073191 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.077065 obj = -8.279057, rho = -1.216770 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 149 nu = 0.074861 obj = -9.623540, rho = -1.615710 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 96.5% (965/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.072639 obj = -10.903274, rho = -2.065705 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) ..*.* optimization finished, #iter = 331 nu = 0.069665 obj = -11.870573, rho = -2.469989 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .....*......* optimization finished, #iter = 1149 nu = 0.061348 obj = -12.329355, rho = -2.692222 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 95.4% (954/1000) (classification) ......*..* optimization finished, #iter = 845 nu = 0.051238 obj = -12.528579, rho = -2.861830 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 95.4% (954/1000) (classification) ......* optimization finished, #iter = 668 nu = 0.040835 obj = -12.575592, rho = -2.913503 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) ......* optimization finished, #iter = 668 nu = 0.032046 obj = -12.575592, rho = -2.913503 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) ......* optimization finished, #iter = 668 nu = 0.025148 obj = -12.575592, rho = -2.913503 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) * optimization finished, #iter = 72 nu = 0.217915 obj = -1.411705, rho = -0.281225 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 95 nu = 0.190302 obj = -1.579419, rho = -0.243997 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.166240 obj = -1.769346, rho = -0.200592 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 57 nu = 0.149231 obj = -1.980117, rho = -0.179438 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 182 nu = 0.134512 obj = -2.192319, rho = -0.232524 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..*...* optimization finished, #iter = 546 nu = 0.115811 obj = -2.392320, rho = -0.262001 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) ...*.* optimization finished, #iter = 423 nu = 0.098633 obj = -2.632857, rho = -0.227420 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) ....*...* optimization finished, #iter = 712 nu = 0.083544 obj = -2.905580, rho = -0.233039 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) ...*......* optimization finished, #iter = 973 nu = 0.072066 obj = -3.241731, rho = -0.203810 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) ..*.....* optimization finished, #iter = 700 nu = 0.064849 obj = -3.600223, rho = -0.150990 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) ...*.*..* optimization finished, #iter = 603 nu = 0.054631 obj = -3.993117, rho = -0.142325 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 248 nu = 0.046418 obj = -4.488875, rho = -0.142171 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 215 nu = 0.040486 obj = -5.116783, rho = -0.129900 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) ...*.* optimization finished, #iter = 492 nu = 0.037151 obj = -5.820313, rho = -0.061629 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) ...*.* optimization finished, #iter = 477 nu = 0.035342 obj = -6.496079, rho = 0.018756 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) ....* optimization finished, #iter = 446 nu = 0.033548 obj = -6.929532, rho = 0.098441 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) .....*........* optimization finished, #iter = 1311 nu = 0.029162 obj = -7.047358, rho = 0.266860 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .....*........* optimization finished, #iter = 1311 nu = 0.022885 obj = -7.047358, rho = 0.266860 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .....*........* optimization finished, #iter = 1311 nu = 0.017959 obj = -7.047358, rho = 0.266860 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .....*........* optimization finished, #iter = 1311 nu = 0.014094 obj = -7.047358, rho = 0.266860 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.249702 obj = -1.779804, rho = 0.093436 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 97% (97/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.224105 obj = -2.059915, rho = 0.074449 nSV = 28, nBSV = 17 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.200249 obj = -2.402413, rho = 0.024042 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..*..* optimization finished, #iter = 456 nu = 0.182287 obj = -2.812468, rho = 0.009828 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 198 nu = 0.162738 obj = -3.317818, rho = 0.017124 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) ...* optimization finished, #iter = 337 nu = 0.148234 obj = -3.954116, rho = 0.011495 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.....* optimization finished, #iter = 626 nu = 0.138725 obj = -4.737884, rho = 0.006891 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 167 nu = 0.131507 obj = -5.671063, rho = -0.003436 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..*...* optimization finished, #iter = 530 nu = 0.121520 obj = -6.800457, rho = 0.028358 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) ........*...* optimization finished, #iter = 1193 nu = 0.113666 obj = -8.185924, rho = 0.032656 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) ........*.* optimization finished, #iter = 981 nu = 0.107096 obj = -9.894432, rho = 0.044597 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 266 nu = 0.098818 obj = -12.001978, rho = 0.047294 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 258 nu = 0.093090 obj = -14.676657, rho = 0.036911 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 180 nu = 0.090654 obj = -17.936286, rho = -0.038865 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 195 nu = 0.089728 obj = -21.761868, rho = -0.159946 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) ...*.* optimization finished, #iter = 407 nu = 0.086899 obj = -26.109608, rho = -0.269056 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) .......*......* optimization finished, #iter = 1349 nu = 0.081160 obj = -31.137583, rho = -0.329302 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) ...........*....* optimization finished, #iter = 1535 nu = 0.075290 obj = -37.404121, rho = -0.409548 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) ........*..* optimization finished, #iter = 1004 nu = 0.071181 obj = -45.055689, rho = -0.538612 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) .........*.* optimization finished, #iter = 1058 nu = 0.068898 obj = -53.950423, rho = -0.774677 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 79 nu = 0.223964 obj = -1.590310, rho = -0.010208 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.201115 obj = -1.838035, rho = 0.110465 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 38 nu = 0.182516 obj = -2.131795, rho = 0.212810 nSV = 20, nBSV = 15 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 37 nu = 0.170280 obj = -2.463946, rho = 0.296137 nSV = 19, nBSV = 15 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 46 nu = 0.154510 obj = -2.826804, rho = 0.434998 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 73 nu = 0.136042 obj = -3.244830, rho = 0.474245 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 75 nu = 0.123791 obj = -3.752088, rho = 0.477575 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 75 nu = 0.113000 obj = -4.316622, rho = 0.401818 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.105418 obj = -4.934414, rho = 0.259073 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.091873 obj = -5.614959, rho = 0.242534 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 178 nu = 0.083831 obj = -6.394662, rho = 0.182039 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 164 nu = 0.078902 obj = -7.178778, rho = 0.036577 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 145 nu = 0.069399 obj = -7.923116, rho = -0.004916 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 168 nu = 0.061302 obj = -8.666260, rho = -0.071507 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 159 nu = 0.053099 obj = -9.406046, rho = -0.153117 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 139 nu = 0.047075 obj = -10.062757, rho = -0.227528 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 172 nu = 0.042354 obj = -10.412509, rho = -0.322355 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 252 nu = 0.033830 obj = -10.417917, rho = -0.335046 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 252 nu = 0.026548 obj = -10.417917, rho = -0.335046 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 252 nu = 0.020834 obj = -10.417917, rho = -0.335046 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 91 nu = 0.214622 obj = -1.417554, rho = -0.413498 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.184437 obj = -1.602379, rho = -0.445416 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.169947 obj = -1.818729, rho = -0.437159 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 72 nu = 0.148547 obj = -2.049708, rho = -0.489559 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 244 nu = 0.130047 obj = -2.316343, rho = -0.518835 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.115009 obj = -2.641540, rho = -0.461502 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 141 nu = 0.104287 obj = -2.990697, rho = -0.376035 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.093130 obj = -3.382509, rho = -0.480556 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 96.2% (962/1000) (classification) .*.* optimization finished, #iter = 205 nu = 0.083634 obj = -3.797052, rho = -0.703720 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 95.8% (958/1000) (classification) ..* optimization finished, #iter = 257 nu = 0.071645 obj = -4.273820, rho = -0.720756 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 95.9% (959/1000) (classification) ..* optimization finished, #iter = 287 nu = 0.062009 obj = -4.872773, rho = -0.729408 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 95.9% (959/1000) (classification) ..* optimization finished, #iter = 280 nu = 0.054011 obj = -5.625286, rho = -0.722608 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 95.6% (956/1000) (classification) ..* optimization finished, #iter = 267 nu = 0.049176 obj = -6.548184, rho = -0.802567 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 95.8% (958/1000) (classification) ..* optimization finished, #iter = 262 nu = 0.045229 obj = -7.607907, rho = -0.945681 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96% (960/1000) (classification) ..*.* optimization finished, #iter = 356 nu = 0.040116 obj = -8.856200, rho = -1.018952 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96% (960/1000) (classification) ...* optimization finished, #iter = 397 nu = 0.035874 obj = -10.427092, rho = -1.114436 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96% (960/1000) (classification) ..*.* optimization finished, #iter = 309 nu = 0.032991 obj = -12.372957, rho = -1.174289 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96% (960/1000) (classification) ...* optimization finished, #iter = 348 nu = 0.031262 obj = -14.667351, rho = -1.235179 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 95.7% (957/1000) (classification) .*...* optimization finished, #iter = 432 nu = 0.028326 obj = -17.364250, rho = -1.268463 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 95.8% (958/1000) (classification) .*.* optimization finished, #iter = 216 nu = 0.026804 obj = -20.672278, rho = -1.500221 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 95 nu = 0.225645 obj = -1.535213, rho = 0.235591 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 151 nu = 0.200642 obj = -1.747374, rho = 0.276855 nSV = 25, nBSV = 15 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 120 nu = 0.176433 obj = -1.998908, rho = 0.286870 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) *..* optimization finished, #iter = 221 nu = 0.156919 obj = -2.298887, rho = 0.345145 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.139805 obj = -2.655828, rho = 0.372761 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 87 nu = 0.125597 obj = -3.093151, rho = 0.381324 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 71 nu = 0.114854 obj = -3.610650, rho = 0.422468 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 195 nu = 0.102079 obj = -4.237028, rho = 0.445288 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*...* optimization finished, #iter = 585 nu = 0.094954 obj = -5.004099, rho = 0.593087 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) ...* optimization finished, #iter = 397 nu = 0.087732 obj = -5.899635, rho = 0.630218 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 96.5% (965/1000) (classification) *.....* optimization finished, #iter = 533 nu = 0.080500 obj = -6.973740, rho = 0.575790 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 95.9% (959/1000) (classification) .*.* optimization finished, #iter = 289 nu = 0.074714 obj = -8.268196, rho = 0.543555 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 176 nu = 0.070144 obj = -9.783962, rho = 0.548806 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 95.8% (958/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.064975 obj = -11.562208, rho = 0.489506 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 95.9% (959/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.061807 obj = -13.599415, rho = 0.440839 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 94.8% (948/1000) (classification) ...*.* optimization finished, #iter = 446 nu = 0.057505 obj = -15.820523, rho = 0.416268 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 94.8% (948/1000) (classification) .*.* optimization finished, #iter = 239 nu = 0.051076 obj = -18.498018, rho = 0.480365 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 94.8% (948/1000) (classification) .*.* optimization finished, #iter = 222 nu = 0.046615 obj = -21.810251, rho = 0.629216 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 95.3% (953/1000) (classification) .* optimization finished, #iter = 125 nu = 0.045201 obj = -25.556539, rho = 0.895006 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 99% (99/100) (classification) Accuracy = 95.2% (952/1000) (classification) .* optimization finished, #iter = 133 nu = 0.044319 obj = -29.221704, rho = 1.270934 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 99% (99/100) (classification) Accuracy = 94.8% (948/1000) (classification) * optimization finished, #iter = 31 nu = 0.216455 obj = -1.499402, rho = 0.137401 nSV = 23, nBSV = 19 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 65 nu = 0.195649 obj = -1.707671, rho = 0.134000 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 55 nu = 0.176560 obj = -1.947817, rho = 0.212839 nSV = 20, nBSV = 15 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.156587 obj = -2.211614, rho = 0.297251 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 71 nu = 0.142574 obj = -2.516394, rho = 0.311802 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.131001 obj = -2.822369, rho = 0.355270 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.113689 obj = -3.140172, rho = 0.353392 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 190 nu = 0.097215 obj = -3.519714, rho = 0.352974 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.086584 obj = -3.970100, rho = 0.349521 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 176 nu = 0.079188 obj = -4.447370, rho = 0.298689 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 217 nu = 0.074307 obj = -4.845146, rho = 0.166267 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) ..*..* optimization finished, #iter = 449 nu = 0.063973 obj = -5.101775, rho = 0.051210 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..*....* optimization finished, #iter = 610 nu = 0.051888 obj = -5.369505, rho = 0.036152 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 250 nu = 0.043639 obj = -5.667924, rho = -0.070899 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 236 nu = 0.039168 obj = -5.829400, rho = -0.313963 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 236 nu = 0.030737 obj = -5.829400, rho = -0.313963 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 236 nu = 0.024121 obj = -5.829400, rho = -0.313963 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 236 nu = 0.018930 obj = -5.829400, rho = -0.313963 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 236 nu = 0.014855 obj = -5.829400, rho = -0.313963 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 236 nu = 0.011658 obj = -5.829400, rho = -0.313963 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 20 nu = 0.200000 obj = -1.394878, rho = -0.016606 nSV = 22, nBSV = 19 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 29 nu = 0.183262 obj = -1.596702, rho = 0.039377 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 36 nu = 0.168418 obj = -1.803360, rho = -0.022611 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 97 nu = 0.148126 obj = -2.028652, rho = 0.061810 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 91 nu = 0.127905 obj = -2.295455, rho = 0.078653 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.116272 obj = -2.602680, rho = -0.053575 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 212 nu = 0.103880 obj = -2.928357, rho = -0.123283 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.095669 obj = -3.259144, rho = -0.251417 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) ..*.* optimization finished, #iter = 315 nu = 0.082480 obj = -3.585506, rho = -0.357864 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ..*.* optimization finished, #iter = 372 nu = 0.071934 obj = -3.954185, rho = -0.362651 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 190 nu = 0.067866 obj = -4.260678, rho = -0.487298 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ...*.* optimization finished, #iter = 412 nu = 0.059603 obj = -4.367921, rho = -0.385989 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ....* optimization finished, #iter = 490 nu = 0.047722 obj = -4.373538, rho = -0.303212 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ....* optimization finished, #iter = 490 nu = 0.037451 obj = -4.373538, rho = -0.303212 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ....* optimization finished, #iter = 490 nu = 0.029390 obj = -4.373538, rho = -0.303212 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ....* optimization finished, #iter = 490 nu = 0.023064 obj = -4.373538, rho = -0.303212 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ....* optimization finished, #iter = 490 nu = 0.018100 obj = -4.373538, rho = -0.303212 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ....* optimization finished, #iter = 490 nu = 0.014204 obj = -4.373538, rho = -0.303212 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ....* optimization finished, #iter = 490 nu = 0.011147 obj = -4.373538, rho = -0.303212 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ....* optimization finished, #iter = 490 nu = 0.008747 obj = -4.373538, rho = -0.303212 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 41 nu = 0.233046 obj = -1.538442, rho = -0.224952 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 98 nu = 0.205558 obj = -1.726453, rho = -0.208371 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.177108 obj = -1.951289, rho = -0.201876 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.159521 obj = -2.212431, rho = -0.129152 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 201 nu = 0.140970 obj = -2.497672, rho = -0.061665 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 200 nu = 0.123754 obj = -2.831051, rho = -0.111595 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 214 nu = 0.107976 obj = -3.236821, rho = -0.119239 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) .**.* optimization finished, #iter = 151 nu = 0.096441 obj = -3.732093, rho = -0.026139 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 164 nu = 0.088275 obj = -4.296709, rho = -0.005262 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*..* optimization finished, #iter = 339 nu = 0.080444 obj = -4.904900, rho = -0.001933 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..* optimization finished, #iter = 297 nu = 0.070717 obj = -5.632530, rho = 0.038722 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 205 nu = 0.062207 obj = -6.527591, rho = 0.022735 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 187 nu = 0.055991 obj = -7.627638, rho = -0.024321 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 143 nu = 0.052943 obj = -8.916267, rho = -0.122148 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 198 nu = 0.048825 obj = -10.262960, rho = -0.202696 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.044684 obj = -11.822701, rho = -0.146625 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 96.1% (961/1000) (classification) * optimization finished, #iter = 98 nu = 0.043131 obj = -13.382332, rho = 0.124419 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) .* optimization finished, #iter = 151 nu = 0.041524 obj = -14.560109, rho = 0.465402 nSV = 8, nBSV = 2 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ..* optimization finished, #iter = 279 nu = 0.038076 obj = -14.943848, rho = 0.565135 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) ..* optimization finished, #iter = 279 nu = 0.029880 obj = -14.943848, rho = 0.565135 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) *.* optimization finished, #iter = 150 nu = 0.183011 obj = -1.182113, rho = -0.129398 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.....* optimization finished, #iter = 569 nu = 0.159440 obj = -1.321025, rho = -0.158860 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.142600 obj = -1.476942, rho = -0.135859 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 260 nu = 0.122561 obj = -1.645039, rho = -0.173797 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .*....* optimization finished, #iter = 519 nu = 0.107792 obj = -1.838401, rho = -0.091630 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*.* optimization finished, #iter = 206 nu = 0.093270 obj = -2.061124, rho = -0.052692 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.087954 obj = -2.282901, rho = 0.105672 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 150 nu = 0.077510 obj = -2.469331, rho = 0.058912 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.065417 obj = -2.642132, rho = 0.019889 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) *.* optimization finished, #iter = 178 nu = 0.055441 obj = -2.814838, rho = -0.013584 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 80 nu = 0.049524 obj = -2.966326, rho = -0.126593 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 123 nu = 0.040340 obj = -3.028074, rho = -0.139349 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..* optimization finished, #iter = 263 nu = 0.033339 obj = -3.055742, rho = -0.095273 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..* optimization finished, #iter = 263 nu = 0.026163 obj = -3.055742, rho = -0.095273 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..* optimization finished, #iter = 263 nu = 0.020532 obj = -3.055742, rho = -0.095273 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..* optimization finished, #iter = 263 nu = 0.016113 obj = -3.055742, rho = -0.095273 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..* optimization finished, #iter = 263 nu = 0.012645 obj = -3.055742, rho = -0.095273 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..* optimization finished, #iter = 263 nu = 0.009923 obj = -3.055742, rho = -0.095273 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..* optimization finished, #iter = 263 nu = 0.007787 obj = -3.055742, rho = -0.095273 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..* optimization finished, #iter = 263 nu = 0.006111 obj = -3.055742, rho = -0.095273 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 58 nu = 0.186562 obj = -1.156188, rho = -0.012295 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 47 nu = 0.163060 obj = -1.262008, rho = -0.039831 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 161 nu = 0.136905 obj = -1.379593, rho = -0.045356 nSV = 21, nBSV = 10 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 55 nu = 0.116855 obj = -1.518370, rho = -0.061281 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 68 nu = 0.103362 obj = -1.669710, rho = -0.046001 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 68 nu = 0.086573 obj = -1.835866, rho = -0.008536 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 49 nu = 0.079407 obj = -2.023712, rho = 0.135300 nSV = 11, nBSV = 6 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 99 nu = 0.072682 obj = -2.140562, rho = -0.016404 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.062369 obj = -2.167862, rho = -0.186565 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.048944 obj = -2.167862, rho = -0.186565 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.038410 obj = -2.167862, rho = -0.186565 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.030142 obj = -2.167862, rho = -0.186565 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.023655 obj = -2.167862, rho = -0.186565 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.018563 obj = -2.167862, rho = -0.186565 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.014568 obj = -2.167862, rho = -0.186565 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.011432 obj = -2.167862, rho = -0.186565 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.008971 obj = -2.167862, rho = -0.186565 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.007040 obj = -2.167862, rho = -0.186565 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.005525 obj = -2.167862, rho = -0.186565 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.004336 obj = -2.167862, rho = -0.186565 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 76 nu = 0.187497 obj = -1.302309, rho = -0.278264 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 38 nu = 0.168997 obj = -1.495765, rho = -0.242244 nSV = 19, nBSV = 14 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 64 nu = 0.152185 obj = -1.711471, rho = -0.165905 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 68 nu = 0.136603 obj = -1.962234, rho = -0.235323 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 294 nu = 0.125113 obj = -2.237442, rho = -0.365213 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 97 nu = 0.111899 obj = -2.542879, rho = -0.450891 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 59 nu = 0.105695 obj = -2.850141, rho = -0.372179 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.094224 obj = -3.116302, rho = -0.433885 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 136 nu = 0.079671 obj = -3.402211, rho = -0.460897 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) .*.* optimization finished, #iter = 219 nu = 0.067778 obj = -3.737096, rho = -0.457855 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*..* optimization finished, #iter = 308 nu = 0.060257 obj = -4.080663, rho = -0.331005 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) ...* optimization finished, #iter = 350 nu = 0.050826 obj = -4.442253, rho = -0.319306 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) ..* optimization finished, #iter = 236 nu = 0.044191 obj = -4.824676, rho = -0.525540 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) ..* optimization finished, #iter = 247 nu = 0.038392 obj = -5.179408, rho = -0.769608 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) .*.* optimization finished, #iter = 220 nu = 0.034292 obj = -5.458727, rho = -1.138998 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.2% (952/1000) (classification) ...*.....* optimization finished, #iter = 878 nu = 0.029019 obj = -5.502539, rho = -1.377869 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) ...*.....* optimization finished, #iter = 878 nu = 0.022773 obj = -5.502539, rho = -1.377869 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) ...*.....* optimization finished, #iter = 878 nu = 0.017871 obj = -5.502539, rho = -1.377869 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) ...*.....* optimization finished, #iter = 878 nu = 0.014025 obj = -5.502539, rho = -1.377869 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) ...*.....* optimization finished, #iter = 878 nu = 0.011006 obj = -5.502539, rho = -1.377869 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 63 nu = 0.209898 obj = -1.380456, rho = -0.014920 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 44 nu = 0.190349 obj = -1.542062, rho = 0.060943 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.170536 obj = -1.697642, rho = 0.022333 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) ..*..* optimization finished, #iter = 483 nu = 0.144998 obj = -1.860090, rho = 0.004460 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 167 nu = 0.123973 obj = -2.048570, rho = 0.027702 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 205 nu = 0.105238 obj = -2.264317, rho = 0.060708 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.090325 obj = -2.530172, rho = 0.085973 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 160 nu = 0.080984 obj = -2.832647, rho = 0.074059 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 155 nu = 0.075041 obj = -3.112207, rho = 0.033242 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.064185 obj = -3.362512, rho = 0.071998 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 173 nu = 0.055634 obj = -3.603471, rho = 0.249630 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .*.* optimization finished, #iter = 266 nu = 0.049200 obj = -3.784090, rho = 0.492396 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) ..*.* optimization finished, #iter = 366 nu = 0.041908 obj = -3.840204, rho = 0.596298 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) ..*.* optimization finished, #iter = 366 nu = 0.032887 obj = -3.840204, rho = 0.596298 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) ..*.* optimization finished, #iter = 366 nu = 0.025809 obj = -3.840204, rho = 0.596298 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) ..*.* optimization finished, #iter = 366 nu = 0.020254 obj = -3.840204, rho = 0.596298 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) ..*.* optimization finished, #iter = 366 nu = 0.015894 obj = -3.840204, rho = 0.596298 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) ..*.* optimization finished, #iter = 366 nu = 0.012473 obj = -3.840204, rho = 0.596298 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) ..*.* optimization finished, #iter = 366 nu = 0.009788 obj = -3.840204, rho = 0.596298 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) ..*.* optimization finished, #iter = 366 nu = 0.007682 obj = -3.840204, rho = 0.596298 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 60 nu = 0.197109 obj = -1.369095, rho = -0.437958 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) *..* optimization finished, #iter = 231 nu = 0.179503 obj = -1.559003, rho = -0.385845 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 148 nu = 0.162099 obj = -1.771845, rho = -0.386004 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.141787 obj = -2.011677, rho = -0.392946 nSV = 20, nBSV = 9 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 90 nu = 0.123784 obj = -2.309629, rho = -0.396313 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 77 nu = 0.115903 obj = -2.642895, rho = -0.426621 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*....* optimization finished, #iter = 510 nu = 0.107153 obj = -2.973773, rho = -0.533766 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 73 nu = 0.094544 obj = -3.330419, rho = -0.548140 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 147 nu = 0.087389 obj = -3.649261, rho = -0.514065 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .**.* optimization finished, #iter = 194 nu = 0.081149 obj = -3.877814, rho = -0.734678 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ....*..* optimization finished, #iter = 606 nu = 0.066721 obj = -4.007289, rho = -0.831886 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) ...* optimization finished, #iter = 387 nu = 0.053879 obj = -4.117383, rho = -0.831360 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..* optimization finished, #iter = 284 nu = 0.043285 obj = -4.242538, rho = -0.837039 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*.* optimization finished, #iter = 330 nu = 0.037044 obj = -4.325948, rho = -0.745842 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*.* optimization finished, #iter = 330 nu = 0.029071 obj = -4.325948, rho = -0.745842 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*.* optimization finished, #iter = 330 nu = 0.022814 obj = -4.325948, rho = -0.745842 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*.* optimization finished, #iter = 330 nu = 0.017903 obj = -4.325948, rho = -0.745842 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*.* optimization finished, #iter = 330 nu = 0.014050 obj = -4.325948, rho = -0.745842 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*.* optimization finished, #iter = 330 nu = 0.011026 obj = -4.325948, rho = -0.745842 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*.* optimization finished, #iter = 330 nu = 0.008653 obj = -4.325948, rho = -0.745842 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 183 nu = 0.157975 obj = -0.965799, rho = 0.179891 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.131799 obj = -1.061883, rho = 0.182210 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.113488 obj = -1.175563, rho = 0.235636 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 132 nu = 0.100960 obj = -1.298200, rho = 0.272232 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 71 nu = 0.088009 obj = -1.425494, rho = 0.262814 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 138 nu = 0.078317 obj = -1.548193, rho = 0.239743 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 98 nu = 0.067063 obj = -1.665724, rho = 0.225572 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.058200 obj = -1.767571, rho = 0.212172 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 147 nu = 0.049782 obj = -1.832495, rho = 0.214012 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 180 nu = 0.040551 obj = -1.872077, rho = 0.218838 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.033453 obj = -1.888233, rho = 0.229230 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.026252 obj = -1.888233, rho = 0.229230 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.020602 obj = -1.888233, rho = 0.229230 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.016168 obj = -1.888233, rho = 0.229230 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.012688 obj = -1.888233, rho = 0.229230 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.009957 obj = -1.888233, rho = 0.229230 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.007814 obj = -1.888233, rho = 0.229230 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.006132 obj = -1.888233, rho = 0.229230 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.004812 obj = -1.888233, rho = 0.229230 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.003776 obj = -1.888233, rho = 0.229230 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 72 nu = 0.194110 obj = -1.243116, rho = 0.031117 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 54 nu = 0.168795 obj = -1.382813, rho = 0.029507 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 48 nu = 0.149011 obj = -1.534034, rho = 0.007104 nSV = 18, nBSV = 13 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 48 nu = 0.138802 obj = -1.674270, rho = 0.040837 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 181 nu = 0.119208 obj = -1.775006, rho = -0.011507 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 230 nu = 0.097132 obj = -1.885484, rho = -0.012913 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 195 nu = 0.080497 obj = -2.021355, rho = -0.020530 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*...* optimization finished, #iter = 480 nu = 0.069108 obj = -2.159321, rho = -0.012271 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 203 nu = 0.057927 obj = -2.297257, rho = -0.000037 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.049156 obj = -2.426248, rho = 0.034691 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.......* optimization finished, #iter = 866 nu = 0.042350 obj = -2.522864, rho = 0.106590 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.035606 obj = -2.560952, rho = 0.108203 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.027942 obj = -2.560952, rho = 0.108203 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.021928 obj = -2.560952, rho = 0.108203 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.017208 obj = -2.560952, rho = 0.108203 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.013504 obj = -2.560952, rho = 0.108203 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.010598 obj = -2.560952, rho = 0.108203 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.008317 obj = -2.560952, rho = 0.108203 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.006526 obj = -2.560952, rho = 0.108203 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.005122 obj = -2.560952, rho = 0.108203 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 159 nu = 0.207362 obj = -1.399163, rho = -0.202830 nSV = 25, nBSV = 15 Total nSV = 25 Accuracy = 97% (97/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 80 nu = 0.181342 obj = -1.592345, rho = -0.187119 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 55 nu = 0.166416 obj = -1.813979, rho = -0.188861 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 88 nu = 0.148197 obj = -2.044553, rho = -0.139676 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.129470 obj = -2.312445, rho = -0.109156 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 178 nu = 0.112828 obj = -2.639174, rho = -0.110415 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 77 nu = 0.104990 obj = -3.023287, rho = -0.003617 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 152 nu = 0.097747 obj = -3.379139, rho = 0.080195 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 129 nu = 0.087441 obj = -3.714942, rho = 0.178258 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 154 nu = 0.073871 obj = -4.063329, rho = 0.195954 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 146 nu = 0.063354 obj = -4.468941, rho = 0.275297 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 192 nu = 0.053769 obj = -4.943462, rho = 0.310364 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..* optimization finished, #iter = 212 nu = 0.047710 obj = -5.497637, rho = 0.423130 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 177 nu = 0.042020 obj = -6.005286, rho = 0.512273 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 290 nu = 0.036767 obj = -6.517823, rho = 0.559822 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 137 nu = 0.033408 obj = -6.961335, rho = 0.503429 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 187 nu = 0.029151 obj = -7.045799, rho = 0.512265 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 187 nu = 0.022876 obj = -7.045799, rho = 0.512265 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 187 nu = 0.017952 obj = -7.045799, rho = 0.512265 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 187 nu = 0.014088 obj = -7.045799, rho = 0.512265 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 128 nu = 0.160882 obj = -1.041600, rho = 0.022412 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 64 nu = 0.142654 obj = -1.162041, rho = 0.048632 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 133 nu = 0.124199 obj = -1.290447, rho = 0.112071 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 141 nu = 0.109665 obj = -1.434738, rho = 0.147163 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .*..* optimization finished, #iter = 320 nu = 0.095235 obj = -1.581862, rho = 0.187610 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..* optimization finished, #iter = 277 nu = 0.081161 obj = -1.754368, rho = 0.191667 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*.* optimization finished, #iter = 376 nu = 0.068838 obj = -1.967089, rho = 0.195076 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) ...*....* optimization finished, #iter = 767 nu = 0.062495 obj = -2.214247, rho = 0.232545 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 155 nu = 0.055359 obj = -2.459313, rho = 0.261418 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) ..*..* optimization finished, #iter = 413 nu = 0.050388 obj = -2.707657, rho = 0.318404 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .*..* optimization finished, #iter = 380 nu = 0.046946 obj = -2.889868, rho = 0.393071 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.4% (954/1000) (classification) .............*....* optimization finished, #iter = 1770 nu = 0.040863 obj = -2.939493, rho = 0.481926 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .............*....* optimization finished, #iter = 1770 nu = 0.032068 obj = -2.939493, rho = 0.481926 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .............*....* optimization finished, #iter = 1770 nu = 0.025165 obj = -2.939493, rho = 0.481926 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .............*....* optimization finished, #iter = 1770 nu = 0.019749 obj = -2.939493, rho = 0.481926 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .............*....* optimization finished, #iter = 1770 nu = 0.015498 obj = -2.939493, rho = 0.481926 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .............*....* optimization finished, #iter = 1770 nu = 0.012162 obj = -2.939493, rho = 0.481926 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .............*....* optimization finished, #iter = 1770 nu = 0.009544 obj = -2.939493, rho = 0.481926 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .............*....* optimization finished, #iter = 1770 nu = 0.007490 obj = -2.939493, rho = 0.481926 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .............*....* optimization finished, #iter = 1770 nu = 0.005878 obj = -2.939493, rho = 0.481926 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 62 nu = 0.157404 obj = -1.011541, rho = -0.419764 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 84 nu = 0.139050 obj = -1.123188, rho = -0.466648 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 57 nu = 0.127320 obj = -1.229845, rho = -0.482939 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 58 nu = 0.109761 obj = -1.324086, rho = -0.469137 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 85 nu = 0.094528 obj = -1.409764, rho = -0.472762 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 133 nu = 0.078009 obj = -1.494224, rho = -0.474044 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*..* optimization finished, #iter = 391 nu = 0.064495 obj = -1.588380, rho = -0.485726 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.054883 obj = -1.690330, rho = -0.511729 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 87 nu = 0.045609 obj = -1.781640, rho = -0.501255 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 130 nu = 0.039052 obj = -1.866446, rho = -0.465031 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.033848 obj = -1.910713, rho = -0.451570 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 127 nu = 0.026565 obj = -1.910713, rho = -0.451571 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 127 nu = 0.020847 obj = -1.910713, rho = -0.451571 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 127 nu = 0.016360 obj = -1.910713, rho = -0.451571 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 127 nu = 0.012838 obj = -1.910713, rho = -0.451571 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 127 nu = 0.010075 obj = -1.910713, rho = -0.451571 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 127 nu = 0.007907 obj = -1.910713, rho = -0.451571 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 127 nu = 0.006205 obj = -1.910713, rho = -0.451571 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 127 nu = 0.004869 obj = -1.910713, rho = -0.451571 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 127 nu = 0.003821 obj = -1.910713, rho = -0.451571 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 97 nu = 0.216783 obj = -1.564205, rho = -0.208060 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 74 nu = 0.197243 obj = -1.818915, rho = -0.217384 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 99 nu = 0.181135 obj = -2.111317, rho = -0.191532 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) ..*....* optimization finished, #iter = 649 nu = 0.161109 obj = -2.456335, rho = -0.113035 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 148 nu = 0.147306 obj = -2.876397, rho = -0.096542 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 224 nu = 0.134226 obj = -3.371751, rho = -0.160352 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.123127 obj = -3.959218, rho = -0.236502 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.115758 obj = -4.639642, rho = -0.325272 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.105813 obj = -5.415225, rho = -0.339889 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 197 nu = 0.098899 obj = -6.306232, rho = -0.281766 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) ..*..* optimization finished, #iter = 474 nu = 0.090716 obj = -7.284141, rho = -0.216201 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 176 nu = 0.079607 obj = -8.467701, rho = -0.209594 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .**....* optimization finished, #iter = 570 nu = 0.070726 obj = -9.970339, rho = -0.210398 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.063691 obj = -11.884274, rho = -0.210050 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.....* optimization finished, #iter = 608 nu = 0.058176 obj = -14.323066, rho = -0.209977 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) .....* optimization finished, #iter = 566 nu = 0.053978 obj = -17.428650, rho = -0.172465 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 199 nu = 0.050907 obj = -21.348681, rho = -0.036273 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.048437 obj = -26.274130, rho = 0.014445 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 216 nu = 0.046541 obj = -32.441285, rho = -0.087579 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 146 nu = 0.045250 obj = -40.104385, rho = -0.298725 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 122 nu = 0.251902 obj = -1.746204, rho = -0.056774 nSV = 30, nBSV = 23 Total nSV = 30 Accuracy = 96% (96/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 89 nu = 0.227927 obj = -1.993681, rho = -0.012888 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 96% (96/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.201054 obj = -2.279085, rho = 0.008531 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 160 nu = 0.178279 obj = -2.630168, rho = 0.004394 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 200 nu = 0.159756 obj = -3.045929, rho = 0.005621 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 96% (96/100) (classification) Accuracy = 98.3% (983/1000) (classification) ..* optimization finished, #iter = 294 nu = 0.144416 obj = -3.540427, rho = 0.015728 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) ...*.* optimization finished, #iter = 402 nu = 0.129083 obj = -4.143422, rho = 0.023885 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 96% (96/100) (classification) Accuracy = 98.1% (981/1000) (classification) ........* optimization finished, #iter = 848 nu = 0.116088 obj = -4.892955, rho = 0.053177 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 96% (96/100) (classification) Accuracy = 98% (980/1000) (classification) ..*.................* optimization finished, #iter = 1950 nu = 0.105481 obj = -5.838057, rho = 0.065208 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 96% (96/100) (classification) Accuracy = 98.1% (981/1000) (classification) ...*....* optimization finished, #iter = 736 nu = 0.098169 obj = -7.010501, rho = 0.082230 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 96% (96/100) (classification) Accuracy = 98.2% (982/1000) (classification) ...*.........* optimization finished, #iter = 1278 nu = 0.091308 obj = -8.432852, rho = 0.086841 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 96% (96/100) (classification) Accuracy = 98% (980/1000) (classification) .....*....* optimization finished, #iter = 908 nu = 0.084869 obj = -10.224406, rho = 0.067905 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 96% (96/100) (classification) Accuracy = 98% (980/1000) (classification) ...*.* optimization finished, #iter = 491 nu = 0.080906 obj = -12.439373, rho = 0.012235 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 97% (97/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..* optimization finished, #iter = 215 nu = 0.078170 obj = -15.104656, rho = -0.121460 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 191 nu = 0.076065 obj = -18.231039, rho = -0.312328 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 96% (960/1000) (classification) ......*....* optimization finished, #iter = 1060 nu = 0.073770 obj = -21.787692, rho = -0.582623 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 95.3% (953/1000) (classification) ..*..* optimization finished, #iter = 448 nu = 0.068415 obj = -25.918944, rho = -0.615349 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 95% (950/1000) (classification) ..*.* optimization finished, #iter = 345 nu = 0.065611 obj = -30.663897, rho = -0.595634 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 94.1% (941/1000) (classification) ..* optimization finished, #iter = 289 nu = 0.061076 obj = -36.168915, rho = -0.947079 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 98% (98/100) (classification) Accuracy = 93% (930/1000) (classification) ........* optimization finished, #iter = 874 nu = 0.057667 obj = -42.434621, rho = -1.174071 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 92.4% (924/1000) (classification) * optimization finished, #iter = 78 nu = 0.191248 obj = -1.347061, rho = -0.590964 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 140 nu = 0.170784 obj = -1.553074, rho = -0.614607 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 81 nu = 0.152304 obj = -1.801934, rho = -0.642350 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 46 nu = 0.139140 obj = -2.100402, rho = -0.635528 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 98 nu = 0.128543 obj = -2.444949, rho = -0.553422 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 57 nu = 0.118894 obj = -2.823971, rho = -0.485660 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 69 nu = 0.108113 obj = -3.248824, rho = -0.500595 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 93 nu = 0.099116 obj = -3.725093, rho = -0.553592 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 165 nu = 0.089660 obj = -4.240467, rho = -0.659952 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 189 nu = 0.083395 obj = -4.791693, rho = -0.784678 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 119 nu = 0.079261 obj = -5.263702, rho = -0.950991 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) ..*.* optimization finished, #iter = 326 nu = 0.072919 obj = -5.496855, rho = -1.096005 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) ..* optimization finished, #iter = 248 nu = 0.058958 obj = -5.603918, rho = -1.111188 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) ..* optimization finished, #iter = 297 nu = 0.047849 obj = -5.677855, rho = -1.020479 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.9% (949/1000) (classification) ...*...* optimization finished, #iter = 601 nu = 0.038219 obj = -5.688097, rho = -0.976232 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.8% (948/1000) (classification) ...*...* optimization finished, #iter = 601 nu = 0.029993 obj = -5.688097, rho = -0.976232 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.8% (948/1000) (classification) ...*...* optimization finished, #iter = 601 nu = 0.023537 obj = -5.688097, rho = -0.976232 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.8% (948/1000) (classification) ...*...* optimization finished, #iter = 601 nu = 0.018471 obj = -5.688097, rho = -0.976232 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.8% (948/1000) (classification) ...*...* optimization finished, #iter = 601 nu = 0.014495 obj = -5.688097, rho = -0.976232 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.8% (948/1000) (classification) ...*...* optimization finished, #iter = 601 nu = 0.011375 obj = -5.688097, rho = -0.976232 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.8% (948/1000) (classification) * optimization finished, #iter = 94 nu = 0.182896 obj = -1.201510, rho = 0.155392 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 161 nu = 0.157577 obj = -1.355240, rho = 0.169187 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 148 nu = 0.136441 obj = -1.547183, rho = 0.168313 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.122605 obj = -1.775798, rho = 0.181054 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 90 nu = 0.108905 obj = -2.046581, rho = 0.273415 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 94 nu = 0.100460 obj = -2.358862, rho = 0.358750 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 128 nu = 0.095834 obj = -2.680249, rho = 0.566840 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..*..* optimization finished, #iter = 408 nu = 0.082758 obj = -3.004134, rho = 0.636775 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) ..*.* optimization finished, #iter = 332 nu = 0.073587 obj = -3.401202, rho = 0.710093 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) ......*.....* optimization finished, #iter = 1198 nu = 0.063641 obj = -3.846961, rho = 0.732514 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 273 nu = 0.056188 obj = -4.393904, rho = 0.765454 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 201 nu = 0.050633 obj = -5.046825, rho = 0.737482 nSV = 8, nBSV = 2 Total nSV = 8 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 211 nu = 0.048268 obj = -5.708821, rho = 0.655425 nSV = 8, nBSV = 2 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 196 nu = 0.046411 obj = -6.237130, rho = 0.551105 nSV = 8, nBSV = 2 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 231 nu = 0.040376 obj = -6.549306, rho = 0.491550 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) ..*.* optimization finished, #iter = 375 nu = 0.034655 obj = -6.726837, rho = 0.434519 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) ..* optimization finished, #iter = 289 nu = 0.027903 obj = -6.744834, rho = 0.415277 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) ..* optimization finished, #iter = 289 nu = 0.021897 obj = -6.744834, rho = 0.415277 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) ..* optimization finished, #iter = 289 nu = 0.017184 obj = -6.744834, rho = 0.415277 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) ..* optimization finished, #iter = 289 nu = 0.013485 obj = -6.744834, rho = 0.415277 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.199974 obj = -1.331988, rho = -0.114617 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.176598 obj = -1.502126, rho = -0.146756 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 89 nu = 0.156225 obj = -1.700903, rho = -0.187274 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.*.* optimization finished, #iter = 280 nu = 0.136161 obj = -1.929349, rho = -0.171442 nSV = 21, nBSV = 10 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 187 nu = 0.120035 obj = -2.200871, rho = -0.173823 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.108209 obj = -2.525670, rho = -0.171185 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 83 nu = 0.102109 obj = -2.871143, rho = -0.162666 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.096807 obj = -3.169064, rho = -0.108434 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 80 nu = 0.084883 obj = -3.416458, rho = -0.032458 nSV = 11, nBSV = 5 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.073444 obj = -3.606766, rho = 0.076436 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.063097 obj = -3.721778, rho = -0.018820 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) ..*.* optimization finished, #iter = 381 nu = 0.050153 obj = -3.808950, rho = -0.029853 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) ..*.* optimization finished, #iter = 303 nu = 0.040216 obj = -3.919095, rho = -0.032735 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) .*.* optimization finished, #iter = 218 nu = 0.034022 obj = -3.974298, rho = -0.092526 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .*.* optimization finished, #iter = 218 nu = 0.026699 obj = -3.974298, rho = -0.092526 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .*.* optimization finished, #iter = 218 nu = 0.020953 obj = -3.974298, rho = -0.092526 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .*.* optimization finished, #iter = 218 nu = 0.016443 obj = -3.974298, rho = -0.092526 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .*.* optimization finished, #iter = 218 nu = 0.012904 obj = -3.974298, rho = -0.092526 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .*.* optimization finished, #iter = 218 nu = 0.010126 obj = -3.974298, rho = -0.092526 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .*.* optimization finished, #iter = 218 nu = 0.007947 obj = -3.974298, rho = -0.092526 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 58 nu = 0.178562 obj = -1.225157, rho = -0.091639 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 96 nu = 0.159623 obj = -1.399090, rho = -0.138139 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 97 nu = 0.145365 obj = -1.593095, rho = -0.250574 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.130731 obj = -1.802751, rho = -0.354079 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.115669 obj = -2.030717, rho = -0.413497 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 88 nu = 0.105235 obj = -2.267316, rho = -0.537125 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 64 nu = 0.092101 obj = -2.523008, rho = -0.630657 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.078712 obj = -2.821211, rho = -0.620315 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 36 nu = 0.071009 obj = -3.167187, rho = -0.639129 nSV = 9, nBSV = 5 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 60 nu = 0.066515 obj = -3.465162, rho = -0.696407 nSV = 9, nBSV = 4 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 153 nu = 0.058242 obj = -3.670510, rho = -0.721297 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 274 nu = 0.048790 obj = -3.840161, rho = -0.672612 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *....* optimization finished, #iter = 412 nu = 0.039555 obj = -4.006541, rho = -0.655162 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 56 nu = 0.034790 obj = -4.158103, rho = -0.558809 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 68 nu = 0.027960 obj = -4.161489, rho = -0.534202 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 68 nu = 0.021942 obj = -4.161489, rho = -0.534202 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 68 nu = 0.017219 obj = -4.161489, rho = -0.534202 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 68 nu = 0.013513 obj = -4.161489, rho = -0.534202 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 68 nu = 0.010604 obj = -4.161489, rho = -0.534202 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 68 nu = 0.008322 obj = -4.161489, rho = -0.534202 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 151 nu = 0.178770 obj = -1.185877, rho = -0.106571 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 75 nu = 0.159247 obj = -1.336655, rho = -0.231826 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 57 nu = 0.139376 obj = -1.504730, rho = -0.385926 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 74 nu = 0.125048 obj = -1.696177, rho = -0.470357 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 286 nu = 0.112318 obj = -1.886880, rho = -0.477409 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 253 nu = 0.098112 obj = -2.093468, rho = -0.439727 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 196 nu = 0.084559 obj = -2.322994, rho = -0.552362 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.072136 obj = -2.598860, rho = -0.595475 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .**..* optimization finished, #iter = 310 nu = 0.062403 obj = -2.936708, rho = -0.682023 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 198 nu = 0.054910 obj = -3.342039, rho = -0.814799 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*...* optimization finished, #iter = 484 nu = 0.049062 obj = -3.825653, rho = -0.914831 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) ..* optimization finished, #iter = 248 nu = 0.046182 obj = -4.336071, rho = -1.089134 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*..* optimization finished, #iter = 368 nu = 0.040303 obj = -4.841557, rho = -1.115000 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*.* optimization finished, #iter = 327 nu = 0.034606 obj = -5.461501, rho = -1.076189 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 230 nu = 0.031075 obj = -6.199421, rho = -1.087945 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 170 nu = 0.029353 obj = -6.934395, rho = -1.272205 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 173 nu = 0.028316 obj = -7.437386, rho = -1.624581 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*.* optimization finished, #iter = 217 nu = 0.024362 obj = -7.503617, rho = -1.807276 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) .*.* optimization finished, #iter = 217 nu = 0.019119 obj = -7.503617, rho = -1.807276 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) .*.* optimization finished, #iter = 217 nu = 0.015003 obj = -7.503617, rho = -1.807276 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 67 nu = 0.196012 obj = -1.295683, rho = -0.145345 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.172318 obj = -1.454400, rho = -0.092023 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 97 nu = 0.158728 obj = -1.633160, rho = 0.013476 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 161 nu = 0.135813 obj = -1.811719, rho = 0.029149 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 92 nu = 0.117758 obj = -2.029419, rho = -0.037118 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 157 nu = 0.102863 obj = -2.270362, rho = -0.004499 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 158 nu = 0.087931 obj = -2.566354, rho = 0.007810 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 176 nu = 0.076160 obj = -2.939514, rho = 0.014236 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 267 nu = 0.067722 obj = -3.400710, rho = 0.017240 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 188 nu = 0.062443 obj = -3.942031, rho = 0.091079 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..* optimization finished, #iter = 264 nu = 0.058903 obj = -4.522668, rho = 0.127861 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 181 nu = 0.053708 obj = -5.136852, rho = 0.134864 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 173 nu = 0.050216 obj = -5.716168, rho = 0.095286 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 214 nu = 0.042892 obj = -6.290280, rho = 0.002676 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 176 nu = 0.039736 obj = -6.868258, rho = 0.078110 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 203 nu = 0.037529 obj = -7.115639, rho = 0.189922 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*.* optimization finished, #iter = 203 nu = 0.029451 obj = -7.115639, rho = 0.189922 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*.* optimization finished, #iter = 203 nu = 0.023112 obj = -7.115639, rho = 0.189922 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*.* optimization finished, #iter = 203 nu = 0.018137 obj = -7.115639, rho = 0.189922 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*.* optimization finished, #iter = 203 nu = 0.014234 obj = -7.115639, rho = 0.189922 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 178 nu = 0.201221 obj = -1.290909, rho = 0.083099 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) .*.* optimization finished, #iter = 207 nu = 0.175333 obj = -1.435368, rho = 0.124764 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 179 nu = 0.148936 obj = -1.607541, rho = 0.137936 nSV = 22, nBSV = 11 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 71 nu = 0.134118 obj = -1.811295, rho = 0.052359 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 94 nu = 0.118311 obj = -2.028734, rho = -0.070618 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 94 nu = 0.103659 obj = -2.270606, rho = -0.204448 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.093301 obj = -2.533348, rho = -0.362773 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.080838 obj = -2.808580, rho = -0.279783 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 82 nu = 0.069673 obj = -3.120966, rho = -0.290435 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.061144 obj = -3.485845, rho = -0.082762 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.052393 obj = -3.898928, rho = 0.092642 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.045196 obj = -4.405707, rho = 0.209617 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.039558 obj = -5.023310, rho = 0.355319 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 127 nu = 0.035225 obj = -5.764361, rho = 0.541884 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..*.* optimization finished, #iter = 339 nu = 0.032196 obj = -6.595148, rho = 0.633871 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..*.* optimization finished, #iter = 311 nu = 0.029683 obj = -7.498865, rho = 0.598729 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ..* optimization finished, #iter = 267 nu = 0.028085 obj = -8.358126, rho = 0.662459 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ...*.* optimization finished, #iter = 468 nu = 0.027155 obj = -8.900044, rho = 0.967021 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ...*.* optimization finished, #iter = 470 nu = 0.022790 obj = -8.944061, rho = 1.077465 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) ...*.* optimization finished, #iter = 470 nu = 0.017885 obj = -8.944061, rho = 1.077465 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 181 nu = 0.221081 obj = -1.512531, rho = -0.123550 nSV = 28, nBSV = 19 Total nSV = 28 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) *.* optimization finished, #iter = 175 nu = 0.197411 obj = -1.722601, rho = -0.058028 nSV = 25, nBSV = 15 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.173286 obj = -1.972232, rho = -0.058551 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) *..* optimization finished, #iter = 206 nu = 0.155807 obj = -2.269395, rho = 0.027405 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 164 nu = 0.141137 obj = -2.609777, rho = 0.072131 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.133815 obj = -2.972011, rho = 0.162184 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.124018 obj = -3.304869, rho = 0.214045 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.107127 obj = -3.631021, rho = 0.283325 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 286 nu = 0.090883 obj = -4.004629, rho = 0.252731 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) ...*.* optimization finished, #iter = 383 nu = 0.082182 obj = -4.391406, rho = 0.374628 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) ..*.* optimization finished, #iter = 353 nu = 0.071743 obj = -4.745487, rho = 0.377854 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) ...*.* optimization finished, #iter = 419 nu = 0.062555 obj = -5.055820, rho = 0.199709 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..*.....*...............* optimization finished, #iter = 2173 nu = 0.054817 obj = -5.249368, rho = -0.033306 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ....*...* optimization finished, #iter = 714 nu = 0.045705 obj = -5.338512, rho = -0.018355 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ....*...* optimization finished, #iter = 714 nu = 0.035868 obj = -5.338512, rho = -0.018355 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ....*...* optimization finished, #iter = 714 nu = 0.028147 obj = -5.338512, rho = -0.018355 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ....*...* optimization finished, #iter = 714 nu = 0.022089 obj = -5.338512, rho = -0.018355 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ....*...* optimization finished, #iter = 714 nu = 0.017335 obj = -5.338512, rho = -0.018355 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ....*...* optimization finished, #iter = 714 nu = 0.013603 obj = -5.338512, rho = -0.018355 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ....*...* optimization finished, #iter = 714 nu = 0.010675 obj = -5.338512, rho = -0.018355 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 124 nu = 0.167233 obj = -1.070141, rho = 0.254613 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.143572 obj = -1.192828, rho = 0.242424 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 69 nu = 0.124390 obj = -1.341588, rho = 0.256193 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 35 nu = 0.113307 obj = -1.509278, rho = 0.292015 nSV = 14, nBSV = 9 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 51 nu = 0.099202 obj = -1.677809, rho = 0.313985 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 57 nu = 0.084696 obj = -1.876553, rho = 0.323653 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 61 nu = 0.073236 obj = -2.119799, rho = 0.336476 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 59 nu = 0.067936 obj = -2.395108, rho = 0.365458 nSV = 10, nBSV = 4 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 43 nu = 0.063894 obj = -2.631821, rho = 0.409252 nSV = 9, nBSV = 3 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 81 nu = 0.057693 obj = -2.782796, rho = 0.487571 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 171 nu = 0.048276 obj = -2.866697, rho = 0.509876 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 154 nu = 0.038902 obj = -2.934644, rho = 0.486625 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 126 nu = 0.031419 obj = -2.996300, rho = 0.485825 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 133 nu = 0.025747 obj = -3.007076, rho = 0.506384 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 133 nu = 0.020205 obj = -3.007076, rho = 0.506384 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 133 nu = 0.015856 obj = -3.007076, rho = 0.506384 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 133 nu = 0.012443 obj = -3.007076, rho = 0.506384 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 133 nu = 0.009765 obj = -3.007076, rho = 0.506384 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 133 nu = 0.007663 obj = -3.007076, rho = 0.506384 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 133 nu = 0.006014 obj = -3.007076, rho = 0.506384 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *...* optimization finished, #iter = 304 nu = 0.169999 obj = -1.066333, rho = -0.157555 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 144 nu = 0.146902 obj = -1.177621, rho = -0.160937 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.*.* optimization finished, #iter = 124 nu = 0.126594 obj = -1.300323, rho = -0.157009 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 199 nu = 0.111087 obj = -1.434597, rho = -0.101769 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 95 nu = 0.097056 obj = -1.578211, rho = -0.128293 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.087661 obj = -1.700932, rho = -0.219760 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 80 nu = 0.073413 obj = -1.811909, rho = -0.283383 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 280 nu = 0.061189 obj = -1.933594, rho = -0.319990 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 141 nu = 0.054876 obj = -2.038912, rho = -0.440657 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 147 nu = 0.046706 obj = -2.068935, rho = -0.536446 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 147 nu = 0.036653 obj = -2.068935, rho = -0.536446 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 147 nu = 0.028764 obj = -2.068935, rho = -0.536446 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 147 nu = 0.022572 obj = -2.068935, rho = -0.536446 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 147 nu = 0.017714 obj = -2.068935, rho = -0.536446 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 147 nu = 0.013901 obj = -2.068935, rho = -0.536446 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 147 nu = 0.010909 obj = -2.068935, rho = -0.536446 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 147 nu = 0.008561 obj = -2.068935, rho = -0.536446 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 147 nu = 0.006718 obj = -2.068935, rho = -0.536446 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 147 nu = 0.005272 obj = -2.068935, rho = -0.536446 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 147 nu = 0.004137 obj = -2.068935, rho = -0.536446 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.198415 obj = -1.321061, rho = 0.028500 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*..* optimization finished, #iter = 340 nu = 0.172900 obj = -1.496987, rho = 0.034973 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.158308 obj = -1.691353, rho = -0.026392 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 93 nu = 0.140173 obj = -1.895493, rho = -0.050611 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 75 nu = 0.124485 obj = -2.130832, rho = -0.083397 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 95 nu = 0.108714 obj = -2.381342, rho = -0.076433 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 168 nu = 0.096020 obj = -2.666871, rho = -0.135642 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 155 nu = 0.083219 obj = -2.984874, rho = -0.270047 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 97 nu = 0.072619 obj = -3.361441, rho = -0.361845 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..*........* optimization finished, #iter = 1024 nu = 0.062630 obj = -3.809766, rho = -0.386767 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.054275 obj = -4.376614, rho = -0.380508 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.047816 obj = -5.092545, rho = -0.378746 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 157 nu = 0.044953 obj = -5.947874, rho = -0.482247 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 164 nu = 0.043200 obj = -6.835365, rho = -0.633052 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..*..* optimization finished, #iter = 400 nu = 0.040199 obj = -7.637828, rho = -0.763937 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*..* optimization finished, #iter = 401 nu = 0.034562 obj = -8.518550, rho = -0.731315 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 133 nu = 0.030993 obj = -9.523161, rho = -0.718002 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.029675 obj = -10.371736, rho = -0.848391 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 193 nu = 0.026969 obj = -10.584921, rho = -0.988526 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 193 nu = 0.021164 obj = -10.584921, rho = -0.988526 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) *.* optimization finished, #iter = 130 nu = 0.208225 obj = -1.321824, rho = 0.218695 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.176696 obj = -1.472627, rho = 0.216638 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.150489 obj = -1.662684, rho = 0.224115 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 46 nu = 0.140000 obj = -1.887783, rho = 0.399343 nSV = 18, nBSV = 13 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 177 nu = 0.124661 obj = -2.095586, rho = 0.496739 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) .*.* optimization finished, #iter = 281 nu = 0.110061 obj = -2.322567, rho = 0.460198 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.095421 obj = -2.565777, rho = 0.473292 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.083822 obj = -2.825161, rho = 0.533522 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) *.* optimization finished, #iter = 138 nu = 0.072322 obj = -3.091503, rho = 0.559058 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) .*..* optimization finished, #iter = 373 nu = 0.062433 obj = -3.383443, rho = 0.523876 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 170 nu = 0.056596 obj = -3.648744, rho = 0.713595 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .*.* optimization finished, #iter = 247 nu = 0.050305 obj = -3.815796, rho = 1.023255 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.1% (951/1000) (classification) *..* optimization finished, #iter = 209 nu = 0.041871 obj = -3.837342, rho = 1.218878 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) *..* optimization finished, #iter = 209 nu = 0.032859 obj = -3.837342, rho = 1.218878 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) *..* optimization finished, #iter = 209 nu = 0.025786 obj = -3.837342, rho = 1.218878 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) *..* optimization finished, #iter = 209 nu = 0.020236 obj = -3.837342, rho = 1.218878 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) *..* optimization finished, #iter = 209 nu = 0.015880 obj = -3.837342, rho = 1.218878 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) *..* optimization finished, #iter = 209 nu = 0.012462 obj = -3.837342, rho = 1.218878 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) *..* optimization finished, #iter = 209 nu = 0.009780 obj = -3.837342, rho = 1.218878 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) *..* optimization finished, #iter = 209 nu = 0.007675 obj = -3.837342, rho = 1.218878 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 61 nu = 0.192586 obj = -1.246260, rho = 0.161768 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 62 nu = 0.167010 obj = -1.392142, rho = 0.145299 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.146957 obj = -1.558544, rho = 0.191811 nSV = 20, nBSV = 9 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 76 nu = 0.128326 obj = -1.747849, rho = 0.169493 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 292 nu = 0.110266 obj = -1.974495, rho = 0.175065 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 99% (990/1000) (classification) .*.* optimization finished, #iter = 239 nu = 0.096598 obj = -2.257748, rho = 0.179571 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.087876 obj = -2.578389, rho = 0.207647 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*...* optimization finished, #iter = 410 nu = 0.079527 obj = -2.930824, rho = 0.259408 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.....* optimization finished, #iter = 632 nu = 0.068568 obj = -3.346677, rho = 0.253464 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 194 nu = 0.061493 obj = -3.862685, rho = 0.213596 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 180 nu = 0.059282 obj = -4.406021, rho = 0.197583 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 255 nu = 0.055063 obj = -4.873122, rho = 0.303382 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*..* optimization finished, #iter = 304 nu = 0.048863 obj = -5.288094, rho = 0.465283 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 180 nu = 0.042980 obj = -5.650004, rho = 0.535786 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .*..* optimization finished, #iter = 332 nu = 0.039010 obj = -5.804358, rho = 0.716579 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .*..* optimization finished, #iter = 332 nu = 0.030613 obj = -5.804358, rho = 0.716579 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .*..* optimization finished, #iter = 332 nu = 0.024024 obj = -5.804358, rho = 0.716579 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .*..* optimization finished, #iter = 332 nu = 0.018853 obj = -5.804358, rho = 0.716579 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .*..* optimization finished, #iter = 332 nu = 0.014795 obj = -5.804358, rho = 0.716579 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .*..* optimization finished, #iter = 332 nu = 0.011611 obj = -5.804358, rho = 0.716579 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 89 nu = 0.156126 obj = -0.978169, rho = -0.242857 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.137810 obj = -1.070578, rho = -0.163638 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.117249 obj = -1.171505, rho = -0.172994 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 91 nu = 0.104659 obj = -1.272342, rho = -0.118879 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.088843 obj = -1.362416, rho = -0.149367 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 96 nu = 0.075248 obj = -1.450695, rho = -0.139358 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 130 nu = 0.062704 obj = -1.542031, rho = -0.093720 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.051743 obj = -1.650157, rho = -0.100284 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *...* optimization finished, #iter = 375 nu = 0.043949 obj = -1.766744, rho = -0.193374 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ...*.......* optimization finished, #iter = 1088 nu = 0.037575 obj = -1.881839, rho = -0.314865 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 185 nu = 0.031371 obj = -1.992227, rho = -0.368338 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..* optimization finished, #iter = 239 nu = 0.027230 obj = -2.075176, rho = -0.486581 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.........* optimization finished, #iter = 1087 nu = 0.022854 obj = -2.104666, rho = -0.587800 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*...............* optimization finished, #iter = 1646 nu = 0.018027 obj = -2.105684, rho = -0.595641 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*...............* optimization finished, #iter = 1646 nu = 0.014147 obj = -2.105684, rho = -0.595641 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*...............* optimization finished, #iter = 1646 nu = 0.011102 obj = -2.105684, rho = -0.595641 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*...............* optimization finished, #iter = 1646 nu = 0.008712 obj = -2.105684, rho = -0.595641 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*...............* optimization finished, #iter = 1646 nu = 0.006837 obj = -2.105684, rho = -0.595641 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*...............* optimization finished, #iter = 1646 nu = 0.005365 obj = -2.105684, rho = -0.595641 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*...............* optimization finished, #iter = 1646 nu = 0.004211 obj = -2.105684, rho = -0.595641 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 27 nu = 0.188877 obj = -1.342736, rho = -0.222467 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 34 nu = 0.180000 obj = -1.538924, rho = -0.331169 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 45 nu = 0.163459 obj = -1.728899, rho = -0.421761 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 92 nu = 0.144788 obj = -1.924380, rho = -0.497618 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 191 nu = 0.123716 obj = -2.150893, rho = -0.501111 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.108726 obj = -2.413800, rho = -0.567539 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.095524 obj = -2.714344, rho = -0.621831 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 82 nu = 0.084405 obj = -3.061278, rho = -0.661790 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 91 nu = 0.077092 obj = -3.429338, rho = -0.707019 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.069717 obj = -3.793946, rho = -0.786113 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.064790 obj = -4.078518, rho = -0.925163 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*..* optimization finished, #iter = 476 nu = 0.055280 obj = -4.228318, rho = -0.979533 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) ................*..........* optimization finished, #iter = 2695 nu = 0.044392 obj = -4.356991, rho = -0.982631 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .......*...* optimization finished, #iter = 1029 nu = 0.036396 obj = -4.484673, rho = -1.020882 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ....* optimization finished, #iter = 482 nu = 0.030630 obj = -4.558570, rho = -1.063602 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) ....* optimization finished, #iter = 482 nu = 0.024037 obj = -4.558570, rho = -1.063602 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) ....* optimization finished, #iter = 482 nu = 0.018863 obj = -4.558570, rho = -1.063602 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) ....* optimization finished, #iter = 482 nu = 0.014803 obj = -4.558570, rho = -1.063602 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) ....* optimization finished, #iter = 482 nu = 0.011617 obj = -4.558570, rho = -1.063602 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) ....* optimization finished, #iter = 482 nu = 0.009116 obj = -4.558570, rho = -1.063602 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 40 nu = 0.226521 obj = -1.554115, rho = 0.093162 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 41 nu = 0.203890 obj = -1.768492, rho = 0.107392 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.188559 obj = -2.001345, rho = -0.057825 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.169365 obj = -2.227162, rho = -0.200669 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) ...*...* optimization finished, #iter = 617 nu = 0.151188 obj = -2.456470, rho = -0.243180 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) ..*.* optimization finished, #iter = 315 nu = 0.127612 obj = -2.705393, rho = -0.214797 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) ...* optimization finished, #iter = 369 nu = 0.110316 obj = -2.996535, rho = -0.188827 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.094622 obj = -3.330418, rho = -0.207074 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 197 nu = 0.080192 obj = -3.741180, rho = -0.207790 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.069433 obj = -4.260682, rho = -0.228891 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 92 nu = 0.064437 obj = -4.845963, rho = -0.140880 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.060985 obj = -5.397737, rho = -0.024145 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.053692 obj = -5.854197, rho = -0.045582 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 133 nu = 0.049252 obj = -6.225007, rho = -0.370178 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 176 nu = 0.042241 obj = -6.286763, rho = -0.579415 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 176 nu = 0.033149 obj = -6.286763, rho = -0.579415 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 176 nu = 0.026014 obj = -6.286763, rho = -0.579415 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 176 nu = 0.020415 obj = -6.286763, rho = -0.579415 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 176 nu = 0.016021 obj = -6.286763, rho = -0.579415 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 176 nu = 0.012572 obj = -6.286763, rho = -0.579415 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 88 nu = 0.229277 obj = -1.564508, rho = -0.165854 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 53 nu = 0.209328 obj = -1.777734, rho = -0.013733 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.182167 obj = -2.006793, rho = 0.022465 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.159732 obj = -2.290652, rho = -0.004658 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 86 nu = 0.141314 obj = -2.625453, rho = -0.050510 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 66 nu = 0.126177 obj = -3.036187, rho = -0.063324 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 67 nu = 0.115743 obj = -3.499964, rho = -0.104972 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 70 nu = 0.105684 obj = -4.032246, rho = -0.097606 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 74 nu = 0.098722 obj = -4.598688, rho = -0.127680 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.091144 obj = -5.141606, rho = -0.117177 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.080754 obj = -5.687549, rho = -0.081437 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 144 nu = 0.072413 obj = -6.210696, rho = -0.032501 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 136 nu = 0.064663 obj = -6.615863, rho = -0.117098 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.054226 obj = -6.934486, rho = -0.210858 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 160 nu = 0.046034 obj = -7.167762, rho = -0.331453 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 239 nu = 0.038042 obj = -7.215882, rho = -0.416432 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 239 nu = 0.029854 obj = -7.215882, rho = -0.416432 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 239 nu = 0.023428 obj = -7.215882, rho = -0.416432 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 239 nu = 0.018385 obj = -7.215882, rho = -0.416432 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 239 nu = 0.014428 obj = -7.215882, rho = -0.416432 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 49 nu = 0.211319 obj = -1.403215, rho = 0.133277 nSV = 22, nBSV = 18 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.184319 obj = -1.583915, rho = 0.080017 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 58 nu = 0.168348 obj = -1.789683, rho = 0.096757 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 74 nu = 0.149326 obj = -1.997807, rho = 0.105560 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 96 nu = 0.132395 obj = -2.220375, rho = 0.087160 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.112756 obj = -2.478867, rho = 0.063698 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 64 nu = 0.099423 obj = -2.788414, rho = 0.037202 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 75 nu = 0.088946 obj = -3.107716, rho = 0.010935 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.079732 obj = -3.427432, rho = 0.094807 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 161 nu = 0.067541 obj = -3.776284, rho = 0.156265 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 162 nu = 0.057117 obj = -4.199028, rho = 0.164424 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 153 nu = 0.051140 obj = -4.692688, rho = 0.044867 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*..* optimization finished, #iter = 360 nu = 0.046915 obj = -5.158356, rho = -0.081992 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.043689 obj = -5.481051, rho = -0.174924 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..*.* optimization finished, #iter = 381 nu = 0.037143 obj = -5.528318, rho = -0.159640 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ..*.* optimization finished, #iter = 386 nu = 0.029147 obj = -5.528318, rho = -0.159178 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ..*.* optimization finished, #iter = 386 nu = 0.022874 obj = -5.528318, rho = -0.159178 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ..*.* optimization finished, #iter = 386 nu = 0.017950 obj = -5.528318, rho = -0.159178 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ..*.* optimization finished, #iter = 386 nu = 0.014087 obj = -5.528318, rho = -0.159178 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ..*.* optimization finished, #iter = 386 nu = 0.011055 obj = -5.528318, rho = -0.159178 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.224898 obj = -1.525233, rho = -0.255937 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.202081 obj = -1.732838, rho = -0.301533 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.179845 obj = -1.963167, rho = -0.284536 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*..* optimization finished, #iter = 307 nu = 0.164066 obj = -2.207898, rho = -0.268230 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 160 nu = 0.148886 obj = -2.450077, rho = -0.262687 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 204 nu = 0.132416 obj = -2.693761, rho = -0.212921 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 197 nu = 0.116237 obj = -2.910732, rho = -0.158266 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 249 nu = 0.102874 obj = -3.081137, rho = -0.134524 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..*.* optimization finished, #iter = 316 nu = 0.084780 obj = -3.211104, rho = -0.135526 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 236 nu = 0.068185 obj = -3.355728, rho = -0.135336 nSV = 15, nBSV = 3 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) ........*..* optimization finished, #iter = 1040 nu = 0.056528 obj = -3.533568, rho = -0.161548 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .....* optimization finished, #iter = 565 nu = 0.045860 obj = -3.715666, rho = -0.155228 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.................................* optimization finished, #iter = 3425 nu = 0.038001 obj = -3.920847, rho = -0.133669 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..*.* optimization finished, #iter = 369 nu = 0.034455 obj = -4.023281, rho = -0.173305 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ...* optimization finished, #iter = 381 nu = 0.027038 obj = -4.023281, rho = -0.173402 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ...* optimization finished, #iter = 381 nu = 0.021218 obj = -4.023281, rho = -0.173402 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ...* optimization finished, #iter = 381 nu = 0.016651 obj = -4.023281, rho = -0.173402 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ...* optimization finished, #iter = 381 nu = 0.013067 obj = -4.023281, rho = -0.173402 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ...* optimization finished, #iter = 381 nu = 0.010255 obj = -4.023281, rho = -0.173402 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ...* optimization finished, #iter = 381 nu = 0.008047 obj = -4.023281, rho = -0.173402 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 88 nu = 0.168238 obj = -1.053312, rho = -0.128883 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 93 nu = 0.144748 obj = -1.159172, rho = -0.177154 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 49 nu = 0.126734 obj = -1.280281, rho = -0.175075 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 84 nu = 0.110878 obj = -1.404559, rho = -0.224857 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 192 nu = 0.095523 obj = -1.531443, rho = -0.273988 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.082107 obj = -1.669426, rho = -0.241602 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 92 nu = 0.071791 obj = -1.805470, rho = -0.266381 nSV = 10, nBSV = 4 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 161 nu = 0.061467 obj = -1.927507, rho = -0.287208 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 161 nu = 0.051185 obj = -2.051360, rho = -0.287120 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) *.* optimization finished, #iter = 145 nu = 0.042134 obj = -2.199823, rho = -0.288429 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.037166 obj = -2.350435, rho = -0.120472 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 121 nu = 0.033640 obj = -2.419786, rho = 0.189979 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .* optimization finished, #iter = 121 nu = 0.026399 obj = -2.419786, rho = 0.189979 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .* optimization finished, #iter = 121 nu = 0.020717 obj = -2.419786, rho = 0.189979 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .* optimization finished, #iter = 121 nu = 0.016258 obj = -2.419786, rho = 0.189979 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .* optimization finished, #iter = 121 nu = 0.012758 obj = -2.419786, rho = 0.189979 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .* optimization finished, #iter = 121 nu = 0.010012 obj = -2.419786, rho = 0.189979 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .* optimization finished, #iter = 121 nu = 0.007857 obj = -2.419786, rho = 0.189979 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .* optimization finished, #iter = 121 nu = 0.006166 obj = -2.419786, rho = 0.189979 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .* optimization finished, #iter = 121 nu = 0.004839 obj = -2.419786, rho = 0.189979 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.209076 obj = -1.363724, rho = -0.065993 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*..* optimization finished, #iter = 353 nu = 0.185685 obj = -1.520184, rho = -0.046701 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 65 nu = 0.159572 obj = -1.700888, rho = -0.058447 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.139675 obj = -1.914336, rho = -0.070940 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 53 nu = 0.129795 obj = -2.136752, rho = 0.027561 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) ..*.* optimization finished, #iter = 340 nu = 0.115814 obj = -2.327551, rho = 0.111789 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.097153 obj = -2.527798, rho = 0.102690 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*...* optimization finished, #iter = 470 nu = 0.083878 obj = -2.753731, rho = 0.059514 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*.* optimization finished, #iter = 371 nu = 0.073296 obj = -2.976398, rho = 0.064104 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 278 nu = 0.061075 obj = -3.206637, rho = 0.076056 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 205 nu = 0.053289 obj = -3.433319, rho = 0.195438 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 168 nu = 0.046812 obj = -3.591583, rho = 0.276561 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ..* optimization finished, #iter = 263 nu = 0.039777 obj = -3.645825, rho = 0.271417 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..* optimization finished, #iter = 263 nu = 0.031215 obj = -3.645825, rho = 0.271417 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..* optimization finished, #iter = 263 nu = 0.024496 obj = -3.645825, rho = 0.271417 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..* optimization finished, #iter = 263 nu = 0.019224 obj = -3.645825, rho = 0.271417 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..* optimization finished, #iter = 263 nu = 0.015086 obj = -3.645825, rho = 0.271417 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..* optimization finished, #iter = 263 nu = 0.011839 obj = -3.645825, rho = 0.271417 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..* optimization finished, #iter = 263 nu = 0.009291 obj = -3.645825, rho = 0.271417 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..* optimization finished, #iter = 263 nu = 0.007291 obj = -3.645825, rho = 0.271417 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.168422 obj = -1.004629, rho = -0.207266 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..* optimization finished, #iter = 268 nu = 0.141170 obj = -1.090023, rho = -0.216596 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *..* optimization finished, #iter = 285 nu = 0.119634 obj = -1.188791, rho = -0.166540 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 77 nu = 0.104501 obj = -1.295569, rho = -0.173146 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *...* optimization finished, #iter = 361 nu = 0.091004 obj = -1.389345, rho = -0.188602 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 269 nu = 0.075351 obj = -1.489855, rho = -0.179090 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..*.* optimization finished, #iter = 354 nu = 0.062454 obj = -1.611040, rho = -0.179815 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 189 nu = 0.052739 obj = -1.757044, rho = -0.194329 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 195 nu = 0.044797 obj = -1.920188, rho = -0.202487 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 166 nu = 0.038095 obj = -2.115790, rho = -0.194797 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.034439 obj = -2.296099, rho = -0.120194 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 71 nu = 0.030827 obj = -2.454288, rho = -0.051924 nSV = 7, nBSV = 1 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 91 nu = 0.027207 obj = -2.493992, rho = 0.037167 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 91 nu = 0.021351 obj = -2.493992, rho = 0.037167 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 91 nu = 0.016756 obj = -2.493992, rho = 0.037167 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 91 nu = 0.013149 obj = -2.493992, rho = 0.037167 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 91 nu = 0.010319 obj = -2.493992, rho = 0.037167 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 91 nu = 0.008098 obj = -2.493992, rho = 0.037167 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 91 nu = 0.006355 obj = -2.493992, rho = 0.037167 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 91 nu = 0.004987 obj = -2.493992, rho = 0.037167 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 87 nu = 0.171273 obj = -1.078113, rho = -0.662363 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 55 nu = 0.150256 obj = -1.191023, rho = -0.742581 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.133649 obj = -1.299627, rho = -0.911039 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.115171 obj = -1.401557, rho = -1.057416 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) *....* optimization finished, #iter = 477 nu = 0.100000 obj = -1.491588, rho = -1.253987 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .* optimization finished, #iter = 134 nu = 0.085246 obj = -1.570204, rho = -1.373177 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) ..* optimization finished, #iter = 275 nu = 0.071163 obj = -1.630361, rho = -1.371566 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) ..*.* optimization finished, #iter = 300 nu = 0.057509 obj = -1.682794, rho = -1.389176 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .*.* optimization finished, #iter = 259 nu = 0.046239 obj = -1.741113, rho = -1.408225 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) .* optimization finished, #iter = 166 nu = 0.038755 obj = -1.796992, rho = -1.441667 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) .* optimization finished, #iter = 180 nu = 0.032164 obj = -1.815600, rho = -1.674986 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .* optimization finished, #iter = 180 nu = 0.025241 obj = -1.815600, rho = -1.674986 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .* optimization finished, #iter = 180 nu = 0.019808 obj = -1.815600, rho = -1.674986 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .* optimization finished, #iter = 180 nu = 0.015545 obj = -1.815600, rho = -1.674986 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .* optimization finished, #iter = 180 nu = 0.012199 obj = -1.815600, rho = -1.674986 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .* optimization finished, #iter = 180 nu = 0.009573 obj = -1.815600, rho = -1.674986 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .* optimization finished, #iter = 180 nu = 0.007513 obj = -1.815600, rho = -1.674986 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .* optimization finished, #iter = 180 nu = 0.005896 obj = -1.815600, rho = -1.674986 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .* optimization finished, #iter = 180 nu = 0.004627 obj = -1.815600, rho = -1.674986 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .* optimization finished, #iter = 180 nu = 0.003631 obj = -1.815600, rho = -1.674986 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 87 nu = 0.168121 obj = -1.128062, rho = -0.254463 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 92 nu = 0.147512 obj = -1.278275, rho = -0.238307 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 71 nu = 0.133086 obj = -1.454213, rho = -0.229986 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 74 nu = 0.121742 obj = -1.635563, rho = -0.170229 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 62 nu = 0.108646 obj = -1.813330, rho = -0.075073 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 63 nu = 0.094711 obj = -2.010994, rho = -0.144704 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.083031 obj = -2.210481, rho = -0.211530 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..*.* optimization finished, #iter = 305 nu = 0.070877 obj = -2.442127, rho = -0.231241 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 177 nu = 0.063323 obj = -2.684742, rho = -0.298848 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*.* optimization finished, #iter = 235 nu = 0.055775 obj = -2.909448, rho = -0.249820 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) ...*.* optimization finished, #iter = 459 nu = 0.047587 obj = -3.123098, rho = -0.292508 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ...* optimization finished, #iter = 321 nu = 0.040253 obj = -3.324529, rho = -0.369342 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) ..* optimization finished, #iter = 288 nu = 0.033690 obj = -3.539782, rho = -0.476407 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ...* optimization finished, #iter = 368 nu = 0.028776 obj = -3.751507, rho = -0.569249 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) .*.* optimization finished, #iter = 279 nu = 0.024778 obj = -3.899297, rho = -0.619631 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .* optimization finished, #iter = 140 nu = 0.020740 obj = -3.933303, rho = -0.642901 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .* optimization finished, #iter = 140 nu = 0.016276 obj = -3.933303, rho = -0.642901 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .* optimization finished, #iter = 140 nu = 0.012772 obj = -3.933303, rho = -0.642901 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .* optimization finished, #iter = 140 nu = 0.010023 obj = -3.933303, rho = -0.642901 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .* optimization finished, #iter = 140 nu = 0.007866 obj = -3.933303, rho = -0.642901 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 38 nu = 0.211608 obj = -1.405165, rho = -0.025008 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 74 nu = 0.186262 obj = -1.579174, rho = -0.066005 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.162895 obj = -1.786712, rho = -0.127955 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.144381 obj = -2.031264, rho = -0.116554 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..*...*.* optimization finished, #iter = 599 nu = 0.127432 obj = -2.307101, rho = -0.081438 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 285 nu = 0.110950 obj = -2.644671, rho = -0.052920 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 154 nu = 0.097191 obj = -3.072126, rho = -0.050854 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.089492 obj = -3.596073, rho = 0.113395 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 119 nu = 0.085482 obj = -4.170041, rho = 0.233801 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 195 nu = 0.077983 obj = -4.777340, rho = 0.243075 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) ...*.* optimization finished, #iter = 428 nu = 0.072561 obj = -5.409271, rho = 0.188216 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..* optimization finished, #iter = 296 nu = 0.063731 obj = -6.094749, rho = 0.149792 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 140 nu = 0.057126 obj = -6.863807, rho = 0.114301 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 146 nu = 0.052809 obj = -7.648028, rho = 0.200518 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 152 nu = 0.046739 obj = -8.308022, rho = 0.339963 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 160 nu = 0.040777 obj = -8.942477, rho = 0.510778 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .*.* optimization finished, #iter = 269 nu = 0.034843 obj = -9.484105, rho = 0.629312 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .*.* optimization finished, #iter = 242 nu = 0.030147 obj = -9.940050, rho = 0.753909 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) ..*.* optimization finished, #iter = 337 nu = 0.025491 obj = -10.004860, rho = 0.784270 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) ..*.* optimization finished, #iter = 337 nu = 0.020005 obj = -10.004860, rho = 0.784270 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 49 nu = 0.199890 obj = -1.405558, rho = -0.306731 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 81 nu = 0.179738 obj = -1.614191, rho = -0.330586 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.159160 obj = -1.866153, rho = -0.351764 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 55 nu = 0.143964 obj = -2.172383, rho = -0.363285 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 74 nu = 0.133785 obj = -2.515471, rho = -0.433679 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 97 nu = 0.119794 obj = -2.910094, rho = -0.437701 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 99 nu = 0.109292 obj = -3.386217, rho = -0.591402 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.100925 obj = -3.920940, rho = -0.841334 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.092859 obj = -4.515095, rho = -1.152685 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 90 nu = 0.083025 obj = -5.170390, rho = -1.345856 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 120 nu = 0.074565 obj = -5.978890, rho = -1.458966 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .*...* optimization finished, #iter = 427 nu = 0.066760 obj = -6.888493, rho = -1.587029 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) ......*.* optimization finished, #iter = 705 nu = 0.058637 obj = -8.033834, rho = -1.586981 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) ..*.......* optimization finished, #iter = 912 nu = 0.052646 obj = -9.479478, rho = -1.636097 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) ......*..* optimization finished, #iter = 815 nu = 0.047451 obj = -11.306454, rho = -1.635662 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) ......*......................* optimization finished, #iter = 2856 nu = 0.043621 obj = -13.616936, rho = -1.650928 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..........*..............................................* optimization finished, #iter = 5606 nu = 0.040302 obj = -16.550219, rho = -1.650934 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) .......*.......................................................* optimization finished, #iter = 6266 nu = 0.037765 obj = -20.286335, rho = -1.653969 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) .......*.........................* optimization finished, #iter = 3242 nu = 0.035753 obj = -25.029609, rho = -1.663146 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) .............*.........* optimization finished, #iter = 2269 nu = 0.034769 obj = -31.001399, rho = -1.696462 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 55 nu = 0.168087 obj = -1.071583, rho = -0.062943 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 89 nu = 0.147514 obj = -1.186300, rho = -0.088427 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.126529 obj = -1.315557, rho = -0.096473 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.110298 obj = -1.460367, rho = -0.076942 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 171 nu = 0.096457 obj = -1.626184, rho = -0.023510 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 93 nu = 0.082859 obj = -1.810308, rho = 0.002190 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 70 nu = 0.076567 obj = -2.003575, rho = 0.180524 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 51 nu = 0.067610 obj = -2.168030, rho = 0.339320 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 81 nu = 0.058756 obj = -2.312754, rho = 0.454883 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 144 nu = 0.049203 obj = -2.436026, rho = 0.481228 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 180 nu = 0.042860 obj = -2.530397, rho = 0.495392 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 187 nu = 0.035574 obj = -2.558451, rho = 0.521896 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 187 nu = 0.027917 obj = -2.558451, rho = 0.521896 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 187 nu = 0.021908 obj = -2.558451, rho = 0.521896 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 187 nu = 0.017193 obj = -2.558451, rho = 0.521896 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 187 nu = 0.013492 obj = -2.558451, rho = 0.521896 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 187 nu = 0.010588 obj = -2.558451, rho = 0.521896 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 187 nu = 0.008309 obj = -2.558451, rho = 0.521896 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 187 nu = 0.006521 obj = -2.558451, rho = 0.521896 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 187 nu = 0.005117 obj = -2.558451, rho = 0.521896 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 39 nu = 0.170313 obj = -1.112939, rho = -0.228423 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 36 nu = 0.155229 obj = -1.233809, rho = -0.384782 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 35 nu = 0.133615 obj = -1.360353, rho = -0.405505 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 28 nu = 0.116238 obj = -1.499356, rho = -0.419253 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 56 nu = 0.102110 obj = -1.642259, rho = -0.386092 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 89 nu = 0.089696 obj = -1.782242, rho = -0.272713 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 206 nu = 0.078709 obj = -1.902184, rho = -0.145175 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.066479 obj = -1.982153, rho = -0.087557 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 92 nu = 0.053567 obj = -2.074147, rho = -0.077232 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 71 nu = 0.047318 obj = -2.144796, rho = -0.093693 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.038094 obj = -2.150303, rho = -0.076820 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.029895 obj = -2.150303, rho = -0.076820 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.023460 obj = -2.150303, rho = -0.076820 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.018411 obj = -2.150303, rho = -0.076820 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.014448 obj = -2.150303, rho = -0.076820 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.011338 obj = -2.150303, rho = -0.076820 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.008898 obj = -2.150303, rho = -0.076820 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.006983 obj = -2.150303, rho = -0.076820 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.005480 obj = -2.150303, rho = -0.076820 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.004300 obj = -2.150303, rho = -0.076820 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 53 nu = 0.172628 obj = -1.140434, rho = -0.327428 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.149787 obj = -1.287790, rho = -0.326049 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.134422 obj = -1.460809, rho = -0.314981 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *..* optimization finished, #iter = 246 nu = 0.119709 obj = -1.644621, rho = -0.318841 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *....* optimization finished, #iter = 474 nu = 0.104858 obj = -1.857883, rho = -0.310942 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.093789 obj = -2.108044, rho = -0.278648 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 86 nu = 0.089647 obj = -2.346532, rho = -0.116316 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 147 nu = 0.080738 obj = -2.513906, rho = -0.037715 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 223 nu = 0.068237 obj = -2.662719, rho = -0.016694 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 186 nu = 0.058139 obj = -2.782222, rho = -0.020336 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.048070 obj = -2.873269, rho = -0.109328 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.040489 obj = -2.920405, rho = -0.182979 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 99 nu = 0.031867 obj = -2.920474, rho = -0.185184 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 99 nu = 0.025008 obj = -2.920474, rho = -0.185184 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 99 nu = 0.019625 obj = -2.920474, rho = -0.185184 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 99 nu = 0.015401 obj = -2.920474, rho = -0.185184 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 99 nu = 0.012086 obj = -2.920474, rho = -0.185184 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 99 nu = 0.009485 obj = -2.920474, rho = -0.185184 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 99 nu = 0.007443 obj = -2.920474, rho = -0.185184 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 99 nu = 0.005841 obj = -2.920474, rho = -0.185184 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 45 nu = 0.166048 obj = -1.115797, rho = -0.216241 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 64 nu = 0.144779 obj = -1.268919, rho = -0.216764 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 68 nu = 0.129291 obj = -1.450120, rho = -0.293762 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 44 nu = 0.117720 obj = -1.653815, rho = -0.362855 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 79 nu = 0.106930 obj = -1.864739, rho = -0.264033 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 72 nu = 0.094118 obj = -2.101548, rho = -0.172169 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 47 nu = 0.085626 obj = -2.353175, rho = -0.167204 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 54 nu = 0.075065 obj = -2.619826, rho = -0.360602 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 91 nu = 0.063725 obj = -2.929341, rho = -0.360439 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.056892 obj = -3.298780, rho = -0.411324 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 49 nu = 0.051097 obj = -3.673039, rho = -0.496084 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 43 nu = 0.045826 obj = -4.062770, rho = -0.642021 nSV = 8, nBSV = 2 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 57 nu = 0.042041 obj = -4.377302, rho = -0.853345 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 55 nu = 0.037506 obj = -4.564142, rho = -0.605754 nSV = 7, nBSV = 1 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 79 nu = 0.030760 obj = -4.578648, rho = -0.512128 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 79 nu = 0.024140 obj = -4.578648, rho = -0.512128 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 79 nu = 0.018944 obj = -4.578648, rho = -0.512128 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 79 nu = 0.014866 obj = -4.578648, rho = -0.512128 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 79 nu = 0.011666 obj = -4.578648, rho = -0.512128 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 79 nu = 0.009155 obj = -4.578648, rho = -0.512128 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.186359 obj = -1.269506, rho = 0.128196 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 49 nu = 0.164166 obj = -1.447875, rho = 0.129387 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 37 nu = 0.147724 obj = -1.657113, rho = 0.081156 nSV = 17, nBSV = 13 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 47 nu = 0.138512 obj = -1.873317, rho = 0.170551 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 97 nu = 0.123116 obj = -2.091150, rho = 0.201992 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 69 nu = 0.109666 obj = -2.318741, rho = 0.170866 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 163 nu = 0.093130 obj = -2.569492, rho = 0.193271 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 96% (960/1000) (classification) .*.* optimization finished, #iter = 251 nu = 0.080896 obj = -2.867608, rho = 0.204771 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 95.7% (957/1000) (classification) .* optimization finished, #iter = 164 nu = 0.071926 obj = -3.190607, rho = 0.115467 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 95.5% (955/1000) (classification) ..*.* optimization finished, #iter = 363 nu = 0.064131 obj = -3.518987, rho = 0.042279 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 95.2% (952/1000) (classification) ...*.* optimization finished, #iter = 440 nu = 0.054615 obj = -3.863515, rho = 0.009187 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 95.2% (952/1000) (classification) .*..* optimization finished, #iter = 306 nu = 0.045835 obj = -4.291517, rho = 0.009044 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 95.2% (952/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.040529 obj = -4.819956, rho = 0.164619 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 95% (950/1000) (classification) .*.* optimization finished, #iter = 264 nu = 0.036893 obj = -5.323945, rho = 0.460534 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.5% (945/1000) (classification) .*.* optimization finished, #iter = 262 nu = 0.032953 obj = -5.793361, rho = 0.696866 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 93.9% (939/1000) (classification) .* optimization finished, #iter = 154 nu = 0.029975 obj = -6.138931, rho = 0.988930 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 93% (930/1000) (classification) .* optimization finished, #iter = 174 nu = 0.025638 obj = -6.194510, rho = 1.129584 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 92.6% (926/1000) (classification) .* optimization finished, #iter = 174 nu = 0.020120 obj = -6.194510, rho = 1.129584 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 92.6% (926/1000) (classification) .* optimization finished, #iter = 174 nu = 0.015789 obj = -6.194510, rho = 1.129584 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 92.6% (926/1000) (classification) .* optimization finished, #iter = 174 nu = 0.012391 obj = -6.194510, rho = 1.129584 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 92.6% (926/1000) (classification) * optimization finished, #iter = 69 nu = 0.168016 obj = -1.080097, rho = -0.230875 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.150120 obj = -1.199504, rho = -0.202610 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 75 nu = 0.133904 obj = -1.315317, rho = -0.340959 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 72 nu = 0.114695 obj = -1.431185, rho = -0.468113 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 200 nu = 0.099846 obj = -1.555771, rho = -0.524908 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 84 nu = 0.090477 obj = -1.649146, rho = -0.612874 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*...* optimization finished, #iter = 431 nu = 0.074837 obj = -1.704041, rho = -0.656561 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ..*.* optimization finished, #iter = 329 nu = 0.060863 obj = -1.749536, rho = -0.630792 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*..* optimization finished, #iter = 383 nu = 0.048762 obj = -1.793763, rho = -0.631220 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ...*...* optimization finished, #iter = 638 nu = 0.040865 obj = -1.823599, rho = -0.615345 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ......*.*..* optimization finished, #iter = 948 nu = 0.032358 obj = -1.826474, rho = -0.603547 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ......*.*..* optimization finished, #iter = 948 nu = 0.025393 obj = -1.826474, rho = -0.603547 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ......*.*..* optimization finished, #iter = 948 nu = 0.019927 obj = -1.826474, rho = -0.603547 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ......*.*..* optimization finished, #iter = 948 nu = 0.015638 obj = -1.826474, rho = -0.603547 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ......*.*..* optimization finished, #iter = 948 nu = 0.012272 obj = -1.826474, rho = -0.603547 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ......*.*..* optimization finished, #iter = 948 nu = 0.009631 obj = -1.826474, rho = -0.603547 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ......*.*..* optimization finished, #iter = 948 nu = 0.007558 obj = -1.826474, rho = -0.603547 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ......*.*..* optimization finished, #iter = 948 nu = 0.005931 obj = -1.826474, rho = -0.603547 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ......*.*..* optimization finished, #iter = 948 nu = 0.004654 obj = -1.826474, rho = -0.603547 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ......*.*..* optimization finished, #iter = 948 nu = 0.003653 obj = -1.826474, rho = -0.603547 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 55 nu = 0.177603 obj = -1.256368, rho = -0.114467 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 87 nu = 0.158749 obj = -1.448877, rho = -0.031620 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 78 nu = 0.142924 obj = -1.683407, rho = -0.054470 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 67 nu = 0.132861 obj = -1.950300, rho = -0.067556 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 167 nu = 0.120087 obj = -2.242702, rho = -0.094082 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 161 nu = 0.108223 obj = -2.596578, rho = -0.040846 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.098230 obj = -2.990491, rho = -0.005561 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.088102 obj = -3.468636, rho = 0.021736 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 89 nu = 0.080945 obj = -4.010501, rho = 0.142685 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 79 nu = 0.075315 obj = -4.621411, rho = 0.209361 nSV = 11, nBSV = 5 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 93 nu = 0.071518 obj = -5.229152, rho = 0.184479 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.066650 obj = -5.767566, rho = -0.044897 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 155 nu = 0.058051 obj = -6.197688, rho = -0.238245 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 130 nu = 0.050340 obj = -6.599381, rho = -0.408315 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 139 nu = 0.044884 obj = -6.834701, rho = -0.619242 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .* optimization finished, #iter = 192 nu = 0.036065 obj = -6.840864, rho = -0.667953 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) .* optimization finished, #iter = 192 nu = 0.028303 obj = -6.840864, rho = -0.667953 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) .* optimization finished, #iter = 192 nu = 0.022211 obj = -6.840864, rho = -0.667953 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) .* optimization finished, #iter = 192 nu = 0.017430 obj = -6.840864, rho = -0.667953 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) .* optimization finished, #iter = 192 nu = 0.013678 obj = -6.840864, rho = -0.667953 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) * optimization finished, #iter = 89 nu = 0.202110 obj = -1.322441, rho = 0.007551 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 93 nu = 0.178226 obj = -1.475029, rho = 0.103675 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 165 nu = 0.153759 obj = -1.657862, rho = 0.088217 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 170 nu = 0.135432 obj = -1.872026, rho = 0.030769 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 95 nu = 0.121397 obj = -2.112162, rho = -0.043110 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 76 nu = 0.109178 obj = -2.362448, rho = -0.160570 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.099319 obj = -2.611219, rho = -0.240873 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 92 nu = 0.091680 obj = -2.816641, rho = -0.256428 nSV = 10, nBSV = 6 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .* optimization finished, #iter = 156 nu = 0.078600 obj = -2.929681, rho = -0.361229 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .* optimization finished, #iter = 197 nu = 0.065406 obj = -3.005620, rho = -0.439044 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.................* optimization finished, #iter = 1845 nu = 0.052436 obj = -3.045941, rho = -0.461647 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 141 nu = 0.041972 obj = -3.087699, rho = -0.463714 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.033755 obj = -3.093676, rho = -0.466973 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.026489 obj = -3.093676, rho = -0.466973 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.020788 obj = -3.093676, rho = -0.466973 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.016313 obj = -3.093676, rho = -0.466973 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.012802 obj = -3.093676, rho = -0.466973 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.010047 obj = -3.093676, rho = -0.466973 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.007884 obj = -3.093676, rho = -0.466973 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.006187 obj = -3.093676, rho = -0.466973 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 56 nu = 0.211223 obj = -1.357732, rho = -0.178561 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 58 nu = 0.182616 obj = -1.517552, rho = -0.162079 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 64 nu = 0.164871 obj = -1.695153, rho = -0.109273 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 63 nu = 0.145570 obj = -1.870056, rho = -0.036741 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 63 nu = 0.123228 obj = -2.066854, rho = -0.015599 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 78 nu = 0.109556 obj = -2.290299, rho = -0.004023 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 96 nu = 0.096799 obj = -2.504297, rho = -0.047953 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 199 nu = 0.083835 obj = -2.703193, rho = -0.073431 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 119 nu = 0.071205 obj = -2.906847, rho = -0.062974 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.059474 obj = -3.120723, rho = -0.072946 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.050959 obj = -3.369201, rho = -0.034738 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 63 nu = 0.046608 obj = -3.555029, rho = 0.096632 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.038993 obj = -3.573921, rho = 0.171327 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.030600 obj = -3.573921, rho = 0.171327 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.024014 obj = -3.573921, rho = 0.171327 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.018845 obj = -3.573921, rho = 0.171327 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.014789 obj = -3.573921, rho = 0.171327 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.011606 obj = -3.573921, rho = 0.171327 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.009108 obj = -3.573921, rho = 0.171327 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.007147 obj = -3.573921, rho = 0.171327 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 44 nu = 0.215938 obj = -1.417432, rho = -0.191590 nSV = 26, nBSV = 20 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 35 nu = 0.188400 obj = -1.593597, rho = -0.211845 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 45 nu = 0.173416 obj = -1.779512, rho = -0.283458 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 39 nu = 0.154653 obj = -1.955101, rho = -0.268944 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 73 nu = 0.133579 obj = -2.132635, rho = -0.219427 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.112919 obj = -2.324281, rho = -0.201129 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 157 nu = 0.094899 obj = -2.551090, rho = -0.214554 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 150 nu = 0.083696 obj = -2.819643, rho = -0.151389 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 211 nu = 0.074128 obj = -3.056453, rho = -0.063857 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 198 nu = 0.063186 obj = -3.302287, rho = -0.028400 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 258 nu = 0.056816 obj = -3.506561, rho = -0.089741 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*..* optimization finished, #iter = 324 nu = 0.046831 obj = -3.634738, rho = -0.029757 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 220 nu = 0.038288 obj = -3.763241, rho = -0.028977 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.032050 obj = -3.877185, rho = 0.026634 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 234 nu = 0.026178 obj = -3.896180, rho = 0.043319 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 234 nu = 0.020544 obj = -3.896180, rho = 0.043319 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 234 nu = 0.016122 obj = -3.896180, rho = 0.043319 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 234 nu = 0.012652 obj = -3.896180, rho = 0.043319 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 234 nu = 0.009929 obj = -3.896180, rho = 0.043319 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 234 nu = 0.007792 obj = -3.896180, rho = 0.043319 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 37 nu = 0.193648 obj = -1.249134, rho = -0.359749 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 44 nu = 0.171357 obj = -1.386663, rho = -0.186240 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 84 nu = 0.148567 obj = -1.538852, rho = -0.272107 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 178 nu = 0.128033 obj = -1.710616, rho = -0.299699 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 157 nu = 0.110226 obj = -1.915285, rho = -0.273894 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 81 nu = 0.098964 obj = -2.148276, rho = -0.251464 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 58 nu = 0.088091 obj = -2.387782, rho = -0.197299 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 90 nu = 0.077706 obj = -2.628907, rho = -0.114477 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 96 nu = 0.068105 obj = -2.873903, rho = -0.139266 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 73 nu = 0.059223 obj = -3.118344, rho = -0.153502 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 66 nu = 0.053138 obj = -3.335881, rho = -0.187476 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.046437 obj = -3.425597, rho = -0.239707 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.037437 obj = -3.431729, rho = -0.267043 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.029379 obj = -3.431729, rho = -0.267043 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.023056 obj = -3.431729, rho = -0.267043 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.018093 obj = -3.431729, rho = -0.267043 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.014199 obj = -3.431729, rho = -0.267043 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.011143 obj = -3.431729, rho = -0.267043 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.008744 obj = -3.431729, rho = -0.267043 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.006862 obj = -3.431729, rho = -0.267043 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) * optimization finished, #iter = 77 nu = 0.186939 obj = -1.259886, rho = -0.074456 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 94 nu = 0.165216 obj = -1.432660, rho = -0.009711 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 42 nu = 0.145185 obj = -1.635803, rho = -0.046563 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 153 nu = 0.130692 obj = -1.867875, rho = -0.214827 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 178 nu = 0.115253 obj = -2.142761, rho = -0.303264 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 80 nu = 0.105522 obj = -2.468239, rho = -0.356592 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 99 nu = 0.099209 obj = -2.806977, rho = -0.275931 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.085839 obj = -3.171859, rho = -0.240583 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 144 nu = 0.078705 obj = -3.584747, rho = -0.286389 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 136 nu = 0.070066 obj = -4.010850, rho = -0.352626 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 193 nu = 0.062786 obj = -4.456000, rho = -0.489688 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 126 nu = 0.059680 obj = -4.816820, rho = -0.683870 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 146 nu = 0.050016 obj = -5.055303, rho = -0.764849 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 183 nu = 0.041486 obj = -5.262582, rho = -0.841161 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 194 nu = 0.034239 obj = -5.471859, rho = -0.916054 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 206 nu = 0.029446 obj = -5.584967, rho = -1.031930 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 206 nu = 0.023108 obj = -5.584967, rho = -1.031930 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 206 nu = 0.018135 obj = -5.584967, rho = -1.031930 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 206 nu = 0.014231 obj = -5.584967, rho = -1.031930 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 206 nu = 0.011168 obj = -5.584967, rho = -1.031930 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 71 nu = 0.148412 obj = -0.925070, rho = -0.316719 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 33 nu = 0.128442 obj = -1.016976, rho = -0.329424 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 70 nu = 0.113674 obj = -1.112743, rho = -0.295715 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.096730 obj = -1.211262, rho = -0.267257 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 75 nu = 0.083818 obj = -1.316957, rho = -0.167882 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.070283 obj = -1.423038, rho = -0.134319 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.059013 obj = -1.551514, rho = -0.190761 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.049990 obj = -1.703248, rho = -0.224441 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 43 nu = 0.043071 obj = -1.879936, rho = -0.262733 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 89 nu = 0.040280 obj = -2.035675, rho = -0.240643 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.035416 obj = -2.134982, rho = -0.321856 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.029878 obj = -2.148876, rho = -0.447186 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.023447 obj = -2.148876, rho = -0.447186 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.018401 obj = -2.148876, rho = -0.447186 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.014440 obj = -2.148876, rho = -0.447186 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.011332 obj = -2.148876, rho = -0.447186 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.008893 obj = -2.148876, rho = -0.447186 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.006979 obj = -2.148876, rho = -0.447186 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.005477 obj = -2.148876, rho = -0.447186 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.004298 obj = -2.148876, rho = -0.447186 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.210981 obj = -1.369957, rho = 0.308009 nSV = 29, nBSV = 18 Total nSV = 29 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 68 nu = 0.191507 obj = -1.520377, rho = 0.466032 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 56 nu = 0.164766 obj = -1.676154, rho = 0.443755 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 67 nu = 0.147604 obj = -1.831497, rho = 0.449307 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 69 nu = 0.127429 obj = -1.985202, rho = 0.375927 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 162 nu = 0.111322 obj = -2.119571, rho = 0.377524 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 200 nu = 0.094023 obj = -2.223887, rho = 0.510093 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 138 nu = 0.077805 obj = -2.320838, rho = 0.536795 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 236 nu = 0.063162 obj = -2.422010, rho = 0.575118 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 269 nu = 0.050967 obj = -2.546298, rho = 0.574870 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 192 nu = 0.041537 obj = -2.704467, rho = 0.583021 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..* optimization finished, #iter = 282 nu = 0.037233 obj = -2.838527, rho = 0.666773 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 220 nu = 0.030323 obj = -2.921535, rho = 0.716253 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.025119 obj = -2.933808, rho = 0.863979 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.019712 obj = -2.933808, rho = 0.863979 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.015469 obj = -2.933808, rho = 0.863979 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.012140 obj = -2.933808, rho = 0.863979 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.009527 obj = -2.933808, rho = 0.863979 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.007476 obj = -2.933808, rho = 0.863979 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.005867 obj = -2.933808, rho = 0.863979 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 93 nu = 0.141194 obj = -0.876863, rho = -0.156336 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.125660 obj = -0.957265, rho = -0.090296 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 74 nu = 0.106779 obj = -1.037756, rho = -0.116759 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 73 nu = 0.090280 obj = -1.123943, rho = -0.097042 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 132 nu = 0.079680 obj = -1.213398, rho = 0.008816 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 130 nu = 0.067494 obj = -1.282460, rho = 0.050275 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 68 nu = 0.056639 obj = -1.355626, rho = 0.088961 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 73 nu = 0.048487 obj = -1.404413, rho = 0.078057 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 56 nu = 0.039688 obj = -1.439887, rho = 0.082925 nSV = 7, nBSV = 1 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 78 nu = 0.032791 obj = -1.452610, rho = 0.092152 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 78 nu = 0.025733 obj = -1.452610, rho = 0.092152 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 78 nu = 0.020194 obj = -1.452610, rho = 0.092152 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 78 nu = 0.015848 obj = -1.452610, rho = 0.092152 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 78 nu = 0.012437 obj = -1.452610, rho = 0.092152 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 78 nu = 0.009760 obj = -1.452610, rho = 0.092152 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 78 nu = 0.007659 obj = -1.452610, rho = 0.092152 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 78 nu = 0.006011 obj = -1.452610, rho = 0.092152 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 78 nu = 0.004717 obj = -1.452610, rho = 0.092152 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 78 nu = 0.003702 obj = -1.452610, rho = 0.092152 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 78 nu = 0.002905 obj = -1.452610, rho = 0.092152 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 64 nu = 0.208355 obj = -1.287260, rho = -0.216101 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .* optimization finished, #iter = 129 nu = 0.180401 obj = -1.409649, rho = -0.272488 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 89 nu = 0.160219 obj = -1.524794, rho = -0.312724 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.137470 obj = -1.633991, rho = -0.261955 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 230 nu = 0.118360 obj = -1.714764, rho = -0.166110 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) ..*.* optimization finished, #iter = 307 nu = 0.096585 obj = -1.794200, rho = -0.138276 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) ....*....* optimization finished, #iter = 835 nu = 0.080357 obj = -1.875584, rho = -0.191227 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..*..* optimization finished, #iter = 481 nu = 0.064718 obj = -1.959239, rho = -0.185387 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.053295 obj = -2.059926, rho = -0.186616 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 149 nu = 0.047248 obj = -2.120333, rho = -0.191891 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 172 nu = 0.037579 obj = -2.121126, rho = -0.196135 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 172 nu = 0.029490 obj = -2.121126, rho = -0.196135 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 172 nu = 0.023143 obj = -2.121126, rho = -0.196135 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 172 nu = 0.018162 obj = -2.121126, rho = -0.196135 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 172 nu = 0.014252 obj = -2.121126, rho = -0.196135 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 172 nu = 0.011185 obj = -2.121126, rho = -0.196135 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 172 nu = 0.008777 obj = -2.121126, rho = -0.196135 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 172 nu = 0.006888 obj = -2.121126, rho = -0.196135 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 172 nu = 0.005406 obj = -2.121126, rho = -0.196135 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 172 nu = 0.004242 obj = -2.121126, rho = -0.196135 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 61 nu = 0.200000 obj = -1.371018, rho = -0.206371 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 99.3% (993/1000) (classification) * optimization finished, #iter = 85 nu = 0.181147 obj = -1.554965, rho = -0.240449 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 67 nu = 0.161770 obj = -1.768100, rho = -0.142901 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 79 nu = 0.141045 obj = -2.008710, rho = -0.136181 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 88 nu = 0.128947 obj = -2.289366, rho = -0.027283 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 98 nu = 0.114045 obj = -2.591358, rho = 0.026233 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 99 nu = 0.106395 obj = -2.912012, rho = 0.273131 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 92 nu = 0.094454 obj = -3.227826, rho = 0.309907 nSV = 12, nBSV = 7 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .* optimization finished, #iter = 188 nu = 0.084324 obj = -3.514605, rho = 0.268069 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*..* optimization finished, #iter = 305 nu = 0.073541 obj = -3.787431, rho = 0.386831 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..* optimization finished, #iter = 239 nu = 0.064627 obj = -4.034777, rho = 0.476307 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ...*.* optimization finished, #iter = 434 nu = 0.053196 obj = -4.204219, rho = 0.519846 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*.* optimization finished, #iter = 344 nu = 0.043454 obj = -4.397770, rho = 0.536291 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..*.* optimization finished, #iter = 300 nu = 0.035583 obj = -4.620787, rho = 0.493263 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 231 nu = 0.031156 obj = -4.773735, rho = 0.293242 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 181 nu = 0.025219 obj = -4.783395, rho = 0.226580 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 181 nu = 0.019791 obj = -4.783395, rho = 0.226580 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 181 nu = 0.015531 obj = -4.783395, rho = 0.226580 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 181 nu = 0.012188 obj = -4.783395, rho = 0.226580 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 181 nu = 0.009565 obj = -4.783395, rho = 0.226580 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.235607 obj = -1.624960, rho = -0.530144 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.215362 obj = -1.841946, rho = -0.581898 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.186896 obj = -2.095318, rho = -0.597779 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..* optimization finished, #iter = 264 nu = 0.162854 obj = -2.410401, rho = -0.602502 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*...* optimization finished, #iter = 413 nu = 0.147080 obj = -2.791165, rho = -0.603733 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 97% (97/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 178 nu = 0.137392 obj = -3.228369, rho = -0.592815 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 236 nu = 0.123045 obj = -3.701211, rho = -0.563121 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 254 nu = 0.108273 obj = -4.279843, rho = -0.598529 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.098709 obj = -4.989358, rho = -0.639360 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 159 nu = 0.094753 obj = -5.742893, rho = -0.697238 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 198 nu = 0.087187 obj = -6.486722, rho = -0.576029 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 143 nu = 0.077786 obj = -7.284679, rho = -0.550317 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) ..* optimization finished, #iter = 290 nu = 0.070463 obj = -8.095838, rho = -0.511181 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) ..*.* optimization finished, #iter = 345 nu = 0.060155 obj = -8.961108, rho = -0.478805 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) ...*.*..* optimization finished, #iter = 650 nu = 0.053164 obj = -9.952357, rho = -0.505096 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) ......*..* optimization finished, #iter = 898 nu = 0.045232 obj = -11.049056, rho = -0.483030 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) ......*.* optimization finished, #iter = 775 nu = 0.038470 obj = -12.402549, rho = -0.487194 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*.* optimization finished, #iter = 362 nu = 0.035438 obj = -13.936328, rho = -0.519427 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*..* optimization finished, #iter = 301 nu = 0.033757 obj = -15.182378, rho = -0.561925 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) ..*.* optimization finished, #iter = 309 nu = 0.031186 obj = -15.594591, rho = -0.652243 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.181459 obj = -1.222253, rho = -0.474143 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.161702 obj = -1.381445, rho = -0.538160 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 93 nu = 0.143613 obj = -1.567002, rho = -0.464238 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 82 nu = 0.133921 obj = -1.754033, rho = -0.351490 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.120842 obj = -1.919705, rho = -0.321535 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*..* optimization finished, #iter = 312 nu = 0.102901 obj = -2.079907, rho = -0.310449 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 75 nu = 0.088722 obj = -2.257420, rho = -0.352257 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.078718 obj = -2.413033, rho = -0.469289 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 279 nu = 0.068624 obj = -2.512548, rho = -0.435839 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 285 nu = 0.056126 obj = -2.571836, rho = -0.424197 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.046109 obj = -2.602318, rho = -0.491040 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 95 nu = 0.036190 obj = -2.602318, rho = -0.491215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 95 nu = 0.028400 obj = -2.602318, rho = -0.491215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 95 nu = 0.022287 obj = -2.602318, rho = -0.491215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 95 nu = 0.017490 obj = -2.602318, rho = -0.491215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 95 nu = 0.013726 obj = -2.602318, rho = -0.491215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 95 nu = 0.010771 obj = -2.602318, rho = -0.491215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 95 nu = 0.008453 obj = -2.602318, rho = -0.491215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 95 nu = 0.006634 obj = -2.602318, rho = -0.491215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 95 nu = 0.005206 obj = -2.602318, rho = -0.491215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 58 nu = 0.161425 obj = -0.983938, rho = -0.132219 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 97 nu = 0.142893 obj = -1.063267, rho = -0.204038 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.121844 obj = -1.137066, rho = -0.215557 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 58 nu = 0.101614 obj = -1.216052, rho = -0.235896 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*..* optimization finished, #iter = 477 nu = 0.083415 obj = -1.305232, rho = -0.224870 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ...*.* optimization finished, #iter = 420 nu = 0.070455 obj = -1.413229, rho = -0.227775 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 156 nu = 0.060405 obj = -1.522729, rho = -0.209945 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 138 nu = 0.052089 obj = -1.633734, rho = -0.213095 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 41 nu = 0.043568 obj = -1.737975, rho = -0.232731 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 43 nu = 0.039353 obj = -1.820679, rho = -0.068350 nSV = 8, nBSV = 2 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 66 nu = 0.032568 obj = -1.838209, rho = 0.084429 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 66 nu = 0.025558 obj = -1.838209, rho = 0.084429 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 66 nu = 0.020057 obj = -1.838209, rho = 0.084429 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 66 nu = 0.015740 obj = -1.838209, rho = 0.084429 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 66 nu = 0.012352 obj = -1.838209, rho = 0.084429 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 66 nu = 0.009693 obj = -1.838209, rho = 0.084429 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 66 nu = 0.007607 obj = -1.838209, rho = 0.084429 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 66 nu = 0.005970 obj = -1.838209, rho = 0.084429 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 66 nu = 0.004685 obj = -1.838209, rho = 0.084429 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 66 nu = 0.003676 obj = -1.838209, rho = 0.084429 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 67 nu = 0.215565 obj = -1.403959, rho = -0.410991 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 64 nu = 0.186643 obj = -1.575022, rho = -0.376403 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 90 nu = 0.163509 obj = -1.774112, rho = -0.416153 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.145090 obj = -2.004757, rho = -0.509876 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 152 nu = 0.128815 obj = -2.261771, rho = -0.554242 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 149 nu = 0.113228 obj = -2.553626, rho = -0.619394 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 117 nu = 0.102399 obj = -2.880866, rho = -0.696756 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 179 nu = 0.089663 obj = -3.227410, rho = -0.745855 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 74 nu = 0.083510 obj = -3.608517, rho = -0.709644 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 166 nu = 0.075057 obj = -3.913583, rho = -0.870338 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 154 nu = 0.062599 obj = -4.219825, rho = -0.880519 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 175 nu = 0.051762 obj = -4.595765, rho = -0.883013 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 195 nu = 0.045919 obj = -5.016216, rho = -0.953026 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 204 nu = 0.039541 obj = -5.400365, rho = -1.002455 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 177 nu = 0.034366 obj = -5.751774, rho = -1.058992 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ..* optimization finished, #iter = 248 nu = 0.031048 obj = -5.962053, rho = -1.176643 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..*...* optimization finished, #iter = 505 nu = 0.024676 obj = -5.963283, rho = -1.190914 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) ..*...* optimization finished, #iter = 505 nu = 0.019365 obj = -5.963283, rho = -1.190914 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) ..*...* optimization finished, #iter = 505 nu = 0.015197 obj = -5.963283, rho = -1.190914 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) ..*...* optimization finished, #iter = 505 nu = 0.011926 obj = -5.963283, rho = -1.190914 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.173337 obj = -1.025051, rho = -0.456906 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 74 nu = 0.151247 obj = -1.100439, rho = -0.490615 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.123896 obj = -1.172773, rho = -0.499038 nSV = 18, nBSV = 7 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 80 nu = 0.105300 obj = -1.257789, rho = -0.546861 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) *....* optimization finished, #iter = 434 nu = 0.088329 obj = -1.336245, rho = -0.561416 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..*.* optimization finished, #iter = 307 nu = 0.073223 obj = -1.424530, rho = -0.587750 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 136 nu = 0.061572 obj = -1.522820, rho = -0.628468 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) . WARNING: using -h 0 may be faster * optimization finished, #iter = 131 nu = 0.053158 obj = -1.612635, rho = -0.643232 nSV = 8, nBSV = 2 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 161 nu = 0.045284 obj = -1.675959, rho = -0.608912 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.038458 obj = -1.703495, rho = -0.636905 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..* optimization finished, #iter = 274 nu = 0.030179 obj = -1.703495, rho = -0.637215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..* optimization finished, #iter = 274 nu = 0.023683 obj = -1.703495, rho = -0.637215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..* optimization finished, #iter = 274 nu = 0.018586 obj = -1.703495, rho = -0.637215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..* optimization finished, #iter = 274 nu = 0.014585 obj = -1.703495, rho = -0.637215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..* optimization finished, #iter = 274 nu = 0.011446 obj = -1.703495, rho = -0.637215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..* optimization finished, #iter = 274 nu = 0.008982 obj = -1.703495, rho = -0.637215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..* optimization finished, #iter = 274 nu = 0.007049 obj = -1.703495, rho = -0.637215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..* optimization finished, #iter = 274 nu = 0.005532 obj = -1.703495, rho = -0.637215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..* optimization finished, #iter = 274 nu = 0.004341 obj = -1.703495, rho = -0.637215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..* optimization finished, #iter = 274 nu = 0.003407 obj = -1.703495, rho = -0.637215 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 80 nu = 0.180538 obj = -1.133993, rho = -0.256029 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 75 nu = 0.159073 obj = -1.247065, rho = -0.349961 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 87 nu = 0.138758 obj = -1.358367, rho = -0.344735 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 61 nu = 0.119554 obj = -1.469993, rho = -0.336607 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 198 nu = 0.102546 obj = -1.582244, rho = -0.314532 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.085813 obj = -1.697617, rho = -0.283529 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 157 nu = 0.073251 obj = -1.816440, rho = -0.300045 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 184 nu = 0.060006 obj = -1.952439, rho = -0.308885 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 238 nu = 0.050949 obj = -2.114062, rho = -0.351230 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ....*..* optimization finished, #iter = 648 nu = 0.044174 obj = -2.285598, rho = -0.316173 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 178 nu = 0.038824 obj = -2.435091, rho = -0.250314 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 284 nu = 0.033207 obj = -2.512139, rho = -0.215269 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.027830 obj = -2.551066, rho = -0.249888 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.021840 obj = -2.551066, rho = -0.249888 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.017139 obj = -2.551066, rho = -0.249888 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.013450 obj = -2.551066, rho = -0.249888 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.010555 obj = -2.551066, rho = -0.249888 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.008283 obj = -2.551066, rho = -0.249888 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.006500 obj = -2.551066, rho = -0.249888 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.005101 obj = -2.551066, rho = -0.249888 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 61 nu = 0.205132 obj = -1.406248, rho = -0.550817 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.181222 obj = -1.605990, rho = -0.577806 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.161074 obj = -1.842860, rho = -0.602441 nSV = 21, nBSV = 10 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 260 nu = 0.144403 obj = -2.128939, rho = -0.671721 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..* optimization finished, #iter = 291 nu = 0.132550 obj = -2.457865, rho = -0.677460 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) ....*...* optimization finished, #iter = 787 nu = 0.118304 obj = -2.824295, rho = -0.645539 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) ...* optimization finished, #iter = 337 nu = 0.104477 obj = -3.277006, rho = -0.599609 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 235 nu = 0.093131 obj = -3.842987, rho = -0.660339 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 250 nu = 0.084850 obj = -4.550456, rho = -0.671091 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 247 nu = 0.080554 obj = -5.379484, rho = -0.721154 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*.* optimization finished, #iter = 253 nu = 0.075327 obj = -6.318413, rho = -0.777095 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 96% (960/1000) (classification) * optimization finished, #iter = 91 nu = 0.071169 obj = -7.370256, rho = -1.127421 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 95.5% (955/1000) (classification) .* optimization finished, #iter = 146 nu = 0.066452 obj = -8.507715, rho = -1.380827 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 94.6% (946/1000) (classification) ..* optimization finished, #iter = 248 nu = 0.063651 obj = -9.653453, rho = -1.620205 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 93.4% (934/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.061436 obj = -10.562379, rho = -1.923322 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 92.3% (923/1000) (classification) ......*.* optimization finished, #iter = 728 nu = 0.057076 obj = -10.890466, rho = -2.189887 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 91.6% (916/1000) (classification) ...........*....* optimization finished, #iter = 1504 nu = 0.045158 obj = -10.912630, rho = -2.202546 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 91.6% (916/1000) (classification) ...........*....* optimization finished, #iter = 1504 nu = 0.035438 obj = -10.912630, rho = -2.202546 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 91.6% (916/1000) (classification) ...........*....* optimization finished, #iter = 1504 nu = 0.027811 obj = -10.912630, rho = -2.202546 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 91.6% (916/1000) (classification) ...........*....* optimization finished, #iter = 1504 nu = 0.021825 obj = -10.912630, rho = -2.202546 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 91.6% (916/1000) (classification) *.* optimization finished, #iter = 171 nu = 0.169179 obj = -1.135454, rho = -0.094176 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.149415 obj = -1.291051, rho = -0.080776 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.134726 obj = -1.458205, rho = -0.043072 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 124 nu = 0.118552 obj = -1.647829, rho = -0.030388 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 146 nu = 0.107768 obj = -1.862091, rho = -0.065550 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 176 nu = 0.096854 obj = -2.079165, rho = -0.160845 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.083634 obj = -2.315228, rho = -0.190435 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.076400 obj = -2.565726, rho = -0.142575 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 120 nu = 0.067979 obj = -2.777961, rho = -0.043532 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..**..* optimization finished, #iter = 417 nu = 0.058868 obj = -2.962829, rho = 0.075678 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ...*.* optimization finished, #iter = 411 nu = 0.049550 obj = -3.133710, rho = -0.004833 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) ........*.* optimization finished, #iter = 929 nu = 0.041974 obj = -3.281613, rho = -0.013701 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ....*....*.......................* optimization finished, #iter = 3104 nu = 0.034338 obj = -3.394639, rho = -0.002458 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..*...* optimization finished, #iter = 516 nu = 0.029618 obj = -3.458659, rho = 0.074091 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*...* optimization finished, #iter = 516 nu = 0.023243 obj = -3.458659, rho = 0.074091 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*...* optimization finished, #iter = 516 nu = 0.018240 obj = -3.458659, rho = 0.074091 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*...* optimization finished, #iter = 516 nu = 0.014314 obj = -3.458659, rho = 0.074091 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*...* optimization finished, #iter = 516 nu = 0.011233 obj = -3.458659, rho = 0.074091 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*...* optimization finished, #iter = 516 nu = 0.008815 obj = -3.458659, rho = 0.074091 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*...* optimization finished, #iter = 516 nu = 0.006918 obj = -3.458659, rho = 0.074091 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 64 nu = 0.214766 obj = -1.546706, rho = -0.062186 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 46 nu = 0.192231 obj = -1.799506, rho = -0.004686 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 48 nu = 0.177059 obj = -2.094501, rho = 0.098737 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 57 nu = 0.165383 obj = -2.425488, rho = 0.067891 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 36 nu = 0.155706 obj = -2.775308, rho = 0.070490 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.145855 obj = -3.109565, rho = -0.064059 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 136 nu = 0.130714 obj = -3.414755, rho = 0.025998 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 251 nu = 0.113826 obj = -3.706393, rho = 0.055083 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*.* optimization finished, #iter = 337 nu = 0.094433 obj = -4.035052, rho = 0.080401 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*.* optimization finished, #iter = 319 nu = 0.084414 obj = -4.358739, rho = 0.174950 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*..*.* optimization finished, #iter = 398 nu = 0.072278 obj = -4.651639, rho = 0.162861 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) ..* optimization finished, #iter = 265 nu = 0.062441 obj = -4.877298, rho = 0.083978 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) ..* optimization finished, #iter = 294 nu = 0.053476 obj = -5.020111, rho = 0.002386 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*.* optimization finished, #iter = 334 nu = 0.043023 obj = -5.024722, rho = -0.016681 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..*.* optimization finished, #iter = 334 nu = 0.033763 obj = -5.024722, rho = -0.016681 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..*.* optimization finished, #iter = 334 nu = 0.026496 obj = -5.024722, rho = -0.016681 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..*.* optimization finished, #iter = 334 nu = 0.020793 obj = -5.024722, rho = -0.016681 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..*.* optimization finished, #iter = 334 nu = 0.016317 obj = -5.024722, rho = -0.016681 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..*.* optimization finished, #iter = 334 nu = 0.012805 obj = -5.024722, rho = -0.016681 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..*.* optimization finished, #iter = 334 nu = 0.010049 obj = -5.024722, rho = -0.016681 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 76 nu = 0.194066 obj = -1.243836, rho = -0.023061 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.169476 obj = -1.388215, rho = -0.003446 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.150057 obj = -1.539084, rho = 0.023722 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.132296 obj = -1.696528, rho = -0.003070 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 295 nu = 0.114221 obj = -1.860077, rho = -0.023517 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..*.........* optimization finished, #iter = 1129 nu = 0.096798 obj = -2.044474, rho = -0.017830 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *...* optimization finished, #iter = 307 nu = 0.081968 obj = -2.272062, rho = -0.008615 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 243 nu = 0.071132 obj = -2.547628, rho = 0.046047 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 212 nu = 0.061552 obj = -2.864558, rho = 0.093924 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 160 nu = 0.055277 obj = -3.238389, rho = 0.024039 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.048781 obj = -3.654670, rho = -0.015733 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 95 nu = 0.045493 obj = -4.074032, rho = -0.006862 nSV = 8, nBSV = 2 Total nSV = 8 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.043449 obj = -4.378349, rho = 0.000840 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 140 nu = 0.037949 obj = -4.432716, rho = 0.009341 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 140 nu = 0.029781 obj = -4.432716, rho = 0.009341 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 140 nu = 0.023371 obj = -4.432716, rho = 0.009341 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 140 nu = 0.018341 obj = -4.432716, rho = 0.009341 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 140 nu = 0.014393 obj = -4.432716, rho = 0.009341 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 140 nu = 0.011295 obj = -4.432716, rho = 0.009341 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 140 nu = 0.008864 obj = -4.432716, rho = 0.009341 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 168 nu = 0.194578 obj = -1.405071, rho = -0.204275 nSV = 27, nBSV = 17 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 72 nu = 0.176407 obj = -1.633958, rho = -0.230014 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 49 nu = 0.156605 obj = -1.911487, rho = -0.242587 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 53 nu = 0.145435 obj = -2.247499, rho = -0.333274 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 64 nu = 0.136499 obj = -2.629353, rho = -0.426293 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 48 nu = 0.127498 obj = -3.044182, rho = -0.586993 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 66 nu = 0.119890 obj = -3.498104, rho = -0.725832 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 178 nu = 0.109048 obj = -3.954565, rho = -0.840134 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 196 nu = 0.096123 obj = -4.465192, rho = -0.928762 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 172 nu = 0.084855 obj = -5.043807, rho = -1.009367 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.078778 obj = -5.653183, rho = -1.210828 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 96.4% (964/1000) (classification) .*.* optimization finished, #iter = 281 nu = 0.069466 obj = -6.216833, rho = -1.345219 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) .* optimization finished, #iter = 181 nu = 0.061119 obj = -6.790022, rho = -1.633419 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) ..*.* optimization finished, #iter = 300 nu = 0.052501 obj = -7.369858, rho = -1.842523 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 95.4% (954/1000) (classification) .*.* optimization finished, #iter = 211 nu = 0.044435 obj = -8.021563, rho = -1.868465 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.4% (954/1000) (classification) ...*.............* optimization finished, #iter = 1604 nu = 0.038796 obj = -8.662611, rho = -2.047718 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 95.4% (954/1000) (classification) .*...* optimization finished, #iter = 427 nu = 0.035126 obj = -9.191977, rho = -2.175004 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) ...*.* optimization finished, #iter = 459 nu = 0.030141 obj = -9.283181, rho = -2.335062 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.4% (954/1000) (classification) ...*.* optimization finished, #iter = 459 nu = 0.023654 obj = -9.283181, rho = -2.335062 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.4% (954/1000) (classification) ...*.* optimization finished, #iter = 459 nu = 0.018563 obj = -9.283181, rho = -2.335062 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.4% (954/1000) (classification) * optimization finished, #iter = 39 nu = 0.191364 obj = -1.296574, rho = -0.148516 nSV = 23, nBSV = 17 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.172930 obj = -1.464625, rho = -0.201150 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.151881 obj = -1.653767, rho = -0.221482 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 53 nu = 0.136111 obj = -1.875439, rho = -0.262821 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 87 nu = 0.120432 obj = -2.108509, rho = -0.198983 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 82 nu = 0.106269 obj = -2.381770, rho = -0.280656 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 91 nu = 0.095681 obj = -2.669649, rho = -0.334653 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 199 nu = 0.084373 obj = -2.991539, rho = -0.347428 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 188 nu = 0.074306 obj = -3.343794, rho = -0.373304 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 226 nu = 0.065236 obj = -3.724356, rho = -0.403231 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 146 nu = 0.057051 obj = -4.157512, rho = -0.437348 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 96.3% (963/1000) (classification) ..*.* optimization finished, #iter = 376 nu = 0.050354 obj = -4.610207, rho = -0.551402 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.1% (961/1000) (classification) ..*.* optimization finished, #iter = 388 nu = 0.042530 obj = -5.151449, rho = -0.551876 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 95.9% (959/1000) (classification) ..*..* optimization finished, #iter = 406 nu = 0.036391 obj = -5.837148, rho = -0.552368 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 95.9% (959/1000) (classification) ..*..* optimization finished, #iter = 415 nu = 0.031573 obj = -6.704432, rho = -0.552908 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 95.5% (955/1000) (classification) ..*.........* optimization finished, #iter = 1195 nu = 0.029607 obj = -7.721143, rho = -0.780581 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 95.4% (954/1000) (classification) ..*.* optimization finished, #iter = 305 nu = 0.028358 obj = -8.709257, rho = -0.978206 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) .*.* optimization finished, #iter = 276 nu = 0.027765 obj = -9.402044, rho = -1.231261 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 94.6% (946/1000) (classification) ...*..*.* optimization finished, #iter = 599 nu = 0.024218 obj = -9.505457, rho = -1.373246 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 94.6% (946/1000) (classification) ...*..*.* optimization finished, #iter = 599 nu = 0.019005 obj = -9.505457, rho = -1.373246 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 94.6% (946/1000) (classification) .* optimization finished, #iter = 161 nu = 0.155291 obj = -0.989539, rho = -0.359434 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.134059 obj = -1.100089, rho = -0.359337 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 74 nu = 0.116649 obj = -1.228570, rho = -0.412418 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 93 nu = 0.101661 obj = -1.372950, rho = -0.432324 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *..* optimization finished, #iter = 239 nu = 0.087023 obj = -1.546396, rho = -0.446069 nSV = 17, nBSV = 6 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 86 nu = 0.079340 obj = -1.750053, rho = -0.521592 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 49 nu = 0.076718 obj = -1.923875, rho = -0.617640 nSV = 11, nBSV = 5 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.068779 obj = -2.020403, rho = -0.663352 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *..* optimization finished, #iter = 219 nu = 0.055347 obj = -2.093824, rho = -0.666801 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*..* optimization finished, #iter = 304 nu = 0.047280 obj = -2.164401, rho = -0.726459 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 139 nu = 0.037937 obj = -2.197069, rho = -0.714129 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 199 nu = 0.030643 obj = -2.204161, rho = -0.590521 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 199 nu = 0.024048 obj = -2.204161, rho = -0.590521 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 199 nu = 0.018872 obj = -2.204161, rho = -0.590521 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 199 nu = 0.014810 obj = -2.204161, rho = -0.590521 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 199 nu = 0.011622 obj = -2.204161, rho = -0.590521 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 199 nu = 0.009121 obj = -2.204161, rho = -0.590521 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 199 nu = 0.007157 obj = -2.204161, rho = -0.590521 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 199 nu = 0.005617 obj = -2.204161, rho = -0.590521 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 199 nu = 0.004408 obj = -2.204161, rho = -0.590521 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 180 nu = 0.173832 obj = -1.114326, rho = 0.009750 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 276 nu = 0.149122 obj = -1.246082, rho = 0.030755 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 177 nu = 0.131055 obj = -1.398109, rho = 0.073355 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 118 nu = 0.113602 obj = -1.576062, rho = 0.104077 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 57 nu = 0.100351 obj = -1.789191, rho = 0.047264 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 158 nu = 0.093257 obj = -2.000700, rho = -0.032931 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 76 nu = 0.082466 obj = -2.218938, rho = 0.003223 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.072332 obj = -2.433589, rho = 0.046665 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 83 nu = 0.063858 obj = -2.658761, rho = 0.118840 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 63 nu = 0.057533 obj = -2.838162, rho = 0.220605 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.048486 obj = -2.954222, rho = 0.273238 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ...*....* optimization finished, #iter = 790 nu = 0.039853 obj = -3.054053, rho = 0.293384 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*.* optimization finished, #iter = 305 nu = 0.033893 obj = -3.105660, rho = 0.353115 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*.* optimization finished, #iter = 305 nu = 0.026598 obj = -3.105660, rho = 0.353115 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*.* optimization finished, #iter = 305 nu = 0.020873 obj = -3.105660, rho = 0.353115 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*.* optimization finished, #iter = 305 nu = 0.016380 obj = -3.105660, rho = 0.353115 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*.* optimization finished, #iter = 305 nu = 0.012855 obj = -3.105660, rho = 0.353115 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*.* optimization finished, #iter = 305 nu = 0.010088 obj = -3.105660, rho = 0.353115 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*.* optimization finished, #iter = 305 nu = 0.007916 obj = -3.105660, rho = 0.353115 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*.* optimization finished, #iter = 305 nu = 0.006212 obj = -3.105660, rho = 0.353115 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 53 nu = 0.151696 obj = -0.925651, rho = 0.299279 nSV = 17, nBSV = 12 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.132874 obj = -1.008397, rho = 0.301426 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.113678 obj = -1.087099, rho = 0.316357 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.094701 obj = -1.171762, rho = 0.321516 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.078756 obj = -1.275612, rho = 0.306823 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.067487 obj = -1.399809, rho = 0.254598 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 55 nu = 0.060200 obj = -1.526785, rho = 0.156375 nSV = 9, nBSV = 3 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 66 nu = 0.051886 obj = -1.634831, rho = 0.097355 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *......* optimization finished, #iter = 676 nu = 0.042771 obj = -1.751671, rho = 0.072484 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 215 nu = 0.035771 obj = -1.891651, rho = 0.070229 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 88 nu = 0.030335 obj = -2.046997, rho = 0.125630 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 40 nu = 0.028115 obj = -2.178031, rho = 0.365934 nSV = 7, nBSV = 1 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 35 nu = 0.023953 obj = -2.195283, rho = 0.489877 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 35 nu = 0.018797 obj = -2.195283, rho = 0.489877 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 35 nu = 0.014751 obj = -2.195283, rho = 0.489877 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 35 nu = 0.011576 obj = -2.195283, rho = 0.489877 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 35 nu = 0.009085 obj = -2.195283, rho = 0.489877 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 35 nu = 0.007129 obj = -2.195283, rho = 0.489877 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 35 nu = 0.005595 obj = -2.195283, rho = 0.489877 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 35 nu = 0.004391 obj = -2.195283, rho = 0.489877 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 73 nu = 0.188779 obj = -1.300144, rho = -0.328774 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 54 nu = 0.167463 obj = -1.486230, rho = -0.323677 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 66 nu = 0.147948 obj = -1.712288, rho = -0.336066 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 71 nu = 0.134681 obj = -1.980251, rho = -0.316742 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 58 nu = 0.122464 obj = -2.279549, rho = -0.319888 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 67 nu = 0.109745 obj = -2.635897, rho = -0.313133 nSV = 14, nBSV = 9 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 89 nu = 0.100473 obj = -3.040469, rho = -0.354330 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 171 nu = 0.095515 obj = -3.471462, rho = -0.306749 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 169 nu = 0.086145 obj = -3.875132, rho = -0.230590 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 168 nu = 0.073542 obj = -4.352162, rho = -0.196987 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 155 nu = 0.065208 obj = -4.932560, rho = -0.176534 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 150 nu = 0.059254 obj = -5.543149, rho = -0.165153 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 150 nu = 0.054095 obj = -6.152308, rho = -0.112339 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 145 nu = 0.048314 obj = -6.709355, rho = 0.041970 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 169 nu = 0.043778 obj = -7.135308, rho = 0.238798 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) ..*.* optimization finished, #iter = 360 nu = 0.038220 obj = -7.247269, rho = 0.404141 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) ..*.* optimization finished, #iter = 360 nu = 0.029994 obj = -7.247269, rho = 0.404141 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) ..*.* optimization finished, #iter = 360 nu = 0.023538 obj = -7.247269, rho = 0.404141 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) ..*.* optimization finished, #iter = 360 nu = 0.018472 obj = -7.247269, rho = 0.404141 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) ..*.* optimization finished, #iter = 360 nu = 0.014496 obj = -7.247269, rho = 0.404141 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 30 nu = 0.205885 obj = -1.435127, rho = -0.055627 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 71 nu = 0.192408 obj = -1.631140, rho = 0.021530 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 51 nu = 0.169900 obj = -1.842930, rho = -0.015477 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 98 nu = 0.151278 obj = -2.076756, rho = -0.003628 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) * optimization finished, #iter = 68 nu = 0.135238 obj = -2.334087, rho = -0.024070 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 71 nu = 0.118686 obj = -2.613640, rho = -0.093503 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 171 nu = 0.104860 obj = -2.929094, rho = -0.092664 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 156 nu = 0.091613 obj = -3.291133, rho = -0.020898 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 197 nu = 0.078640 obj = -3.725042, rho = -0.022869 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.070688 obj = -4.239278, rho = 0.052513 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 177 nu = 0.064779 obj = -4.781565, rho = 0.130128 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.2% (962/1000) (classification) .* optimization finished, #iter = 144 nu = 0.058864 obj = -5.331810, rho = 0.013439 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96% (960/1000) (classification) .*.* optimization finished, #iter = 258 nu = 0.052619 obj = -5.833899, rho = 0.046253 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 95.2% (952/1000) (classification) .*.* optimization finished, #iter = 211 nu = 0.043780 obj = -6.384172, rho = 0.061814 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 95.6% (956/1000) (classification) .* optimization finished, #iter = 173 nu = 0.039000 obj = -7.026032, rho = 0.027552 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.5% (955/1000) (classification) ..*.* optimization finished, #iter = 333 nu = 0.035179 obj = -7.522649, rho = 0.031480 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.4% (954/1000) (classification) .*.* optimization finished, #iter = 229 nu = 0.031929 obj = -7.717654, rho = 0.097829 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) .*.* optimization finished, #iter = 229 nu = 0.025057 obj = -7.717654, rho = 0.097829 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) .*.* optimization finished, #iter = 229 nu = 0.019663 obj = -7.717654, rho = 0.097829 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) .*.* optimization finished, #iter = 229 nu = 0.015431 obj = -7.717654, rho = 0.097829 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) * optimization finished, #iter = 46 nu = 0.181354 obj = -1.166700, rho = -0.124047 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 97 nu = 0.161403 obj = -1.290628, rho = -0.137198 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 68 nu = 0.141226 obj = -1.418898, rho = -0.106586 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 148 nu = 0.121883 obj = -1.558728, rho = -0.088575 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.107637 obj = -1.693421, rho = -0.023453 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 220 nu = 0.090587 obj = -1.833815, rho = -0.003715 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 231 nu = 0.075329 obj = -2.004532, rho = -0.011613 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 180 nu = 0.065343 obj = -2.208607, rho = 0.017373 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 132 nu = 0.060000 obj = -2.398076, rho = 0.146853 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 156 nu = 0.050795 obj = -2.522846, rho = 0.243057 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*..* optimization finished, #iter = 319 nu = 0.041226 obj = -2.671327, rho = 0.250241 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.034514 obj = -2.845044, rho = 0.180174 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 66 nu = 0.030559 obj = -2.988873, rho = 0.167978 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 146 nu = 0.025811 obj = -3.014907, rho = 0.172938 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 146 nu = 0.020255 obj = -3.014907, rho = 0.172938 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 146 nu = 0.015895 obj = -3.014907, rho = 0.172938 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 146 nu = 0.012474 obj = -3.014907, rho = 0.172938 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 146 nu = 0.009789 obj = -3.014907, rho = 0.172938 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 146 nu = 0.007682 obj = -3.014907, rho = 0.172938 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 146 nu = 0.006029 obj = -3.014907, rho = 0.172938 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 39 nu = 0.177734 obj = -1.106746, rho = 0.194286 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 54 nu = 0.154591 obj = -1.211641, rho = 0.152600 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.135446 obj = -1.320734, rho = 0.095851 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 139 nu = 0.115336 obj = -1.433366, rho = 0.005345 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 85 nu = 0.100925 obj = -1.547170, rho = -0.008486 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 73 nu = 0.084982 obj = -1.651979, rho = 0.025255 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 70 nu = 0.072515 obj = -1.754570, rho = 0.071739 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 66 nu = 0.064067 obj = -1.826425, rho = 0.213281 nSV = 9, nBSV = 3 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 63 nu = 0.052824 obj = -1.836383, rho = 0.164774 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 63 nu = 0.041454 obj = -1.836383, rho = 0.164774 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 63 nu = 0.032531 obj = -1.836383, rho = 0.164774 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 63 nu = 0.025529 obj = -1.836383, rho = 0.164774 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 63 nu = 0.020034 obj = -1.836383, rho = 0.164774 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 63 nu = 0.015722 obj = -1.836383, rho = 0.164774 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 63 nu = 0.012338 obj = -1.836383, rho = 0.164774 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 63 nu = 0.009682 obj = -1.836383, rho = 0.164774 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 63 nu = 0.007598 obj = -1.836383, rho = 0.164774 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 63 nu = 0.005963 obj = -1.836383, rho = 0.164774 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 63 nu = 0.004679 obj = -1.836383, rho = 0.164774 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 63 nu = 0.003672 obj = -1.836383, rho = 0.164774 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *..* optimization finished, #iter = 241 nu = 0.210492 obj = -1.413852, rho = -0.080586 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 98 nu = 0.184647 obj = -1.607284, rho = -0.038659 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 88 nu = 0.166583 obj = -1.831185, rho = -0.067270 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.151249 obj = -2.067979, rho = -0.116568 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 241 nu = 0.137672 obj = -2.306418, rho = -0.122618 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 160 nu = 0.120091 obj = -2.558606, rho = -0.119275 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 184 nu = 0.108163 obj = -2.799009, rho = -0.035762 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 157 nu = 0.094253 obj = -3.042385, rho = 0.025264 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 164 nu = 0.080676 obj = -3.277783, rho = 0.083141 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) ..* optimization finished, #iter = 289 nu = 0.067761 obj = -3.512649, rho = 0.136205 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) ..*.......* optimization finished, #iter = 955 nu = 0.056191 obj = -3.786498, rho = 0.144074 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .*.* optimization finished, #iter = 216 nu = 0.050214 obj = -4.061949, rho = 0.284737 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) .*.* optimization finished, #iter = 290 nu = 0.041965 obj = -4.264260, rho = 0.414766 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) ..*.* optimization finished, #iter = 368 nu = 0.034744 obj = -4.466985, rho = 0.504301 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) ..*..* optimization finished, #iter = 431 nu = 0.029514 obj = -4.636793, rho = 0.474216 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .....*.* optimization finished, #iter = 689 nu = 0.024787 obj = -4.701177, rho = 0.478275 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .....*.* optimization finished, #iter = 689 nu = 0.019452 obj = -4.701177, rho = 0.478275 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .....*.* optimization finished, #iter = 689 nu = 0.015265 obj = -4.701177, rho = 0.478275 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .....*.* optimization finished, #iter = 689 nu = 0.011980 obj = -4.701177, rho = 0.478275 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .....*.* optimization finished, #iter = 689 nu = 0.009401 obj = -4.701177, rho = 0.478275 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) * optimization finished, #iter = 43 nu = 0.163759 obj = -1.045655, rho = -0.033934 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 85 nu = 0.144385 obj = -1.155464, rho = -0.066988 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 130 nu = 0.125011 obj = -1.277791, rho = -0.114179 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 59 nu = 0.105377 obj = -1.421055, rho = -0.079331 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.096604 obj = -1.575700, rho = -0.173228 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 196 nu = 0.081826 obj = -1.732241, rho = -0.143030 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.070355 obj = -1.925729, rho = -0.091486 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 93 nu = 0.064920 obj = -2.113447, rho = 0.112511 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.058463 obj = -2.247994, rho = 0.348963 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 221 nu = 0.050320 obj = -2.309674, rho = 0.526902 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 205 nu = 0.041401 obj = -2.336554, rho = 0.611530 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 206 nu = 0.032490 obj = -2.336554, rho = 0.611290 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 206 nu = 0.025497 obj = -2.336554, rho = 0.611290 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 206 nu = 0.020009 obj = -2.336554, rho = 0.611290 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 206 nu = 0.015702 obj = -2.336554, rho = 0.611290 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 206 nu = 0.012322 obj = -2.336554, rho = 0.611290 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 206 nu = 0.009670 obj = -2.336554, rho = 0.611290 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 206 nu = 0.007589 obj = -2.336554, rho = 0.611290 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 206 nu = 0.005955 obj = -2.336554, rho = 0.611290 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 206 nu = 0.004673 obj = -2.336554, rho = 0.611290 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 90 nu = 0.155205 obj = -0.951008, rho = -0.085921 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.130919 obj = -1.043674, rho = -0.071470 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 72 nu = 0.111336 obj = -1.157212, rho = -0.062316 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 90 nu = 0.096710 obj = -1.288000, rho = 0.006306 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 82 nu = 0.083138 obj = -1.441616, rho = 0.027302 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 93 nu = 0.074725 obj = -1.617723, rho = -0.008229 nSV = 11, nBSV = 5 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 80 nu = 0.070269 obj = -1.775310, rho = -0.002214 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 83 nu = 0.063403 obj = -1.874839, rho = 0.122190 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.052839 obj = -1.936485, rho = 0.154187 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 275 nu = 0.044171 obj = -1.956731, rho = 0.281297 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 275 nu = 0.034664 obj = -1.956731, rho = 0.281297 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 275 nu = 0.027203 obj = -1.956731, rho = 0.281297 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 275 nu = 0.021348 obj = -1.956731, rho = 0.281297 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 275 nu = 0.016753 obj = -1.956731, rho = 0.281297 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 275 nu = 0.013147 obj = -1.956731, rho = 0.281297 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 275 nu = 0.010317 obj = -1.956731, rho = 0.281297 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 275 nu = 0.008096 obj = -1.956731, rho = 0.281297 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 275 nu = 0.006354 obj = -1.956731, rho = 0.281297 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 275 nu = 0.004986 obj = -1.956731, rho = 0.281297 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 275 nu = 0.003913 obj = -1.956731, rho = 0.281297 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 52 nu = 0.188158 obj = -1.316493, rho = 0.043724 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 51 nu = 0.171521 obj = -1.508976, rho = 0.014031 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 69 nu = 0.151944 obj = -1.727226, rho = 0.010940 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 172 nu = 0.136875 obj = -1.981602, rho = -0.013689 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 61 nu = 0.122695 obj = -2.278850, rho = 0.084274 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 52 nu = 0.112041 obj = -2.623803, rho = 0.114297 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 68 nu = 0.103762 obj = -2.983070, rho = 0.026889 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.090674 obj = -3.392458, rho = 0.015923 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 152 nu = 0.081014 obj = -3.871940, rho = 0.078681 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 83 nu = 0.074562 obj = -4.401570, rho = 0.101334 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.069925 obj = -4.910367, rho = 0.102680 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*.* optimization finished, #iter = 244 nu = 0.063587 obj = -5.302684, rho = 0.125319 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) ..*.* optimization finished, #iter = 364 nu = 0.052701 obj = -5.671022, rho = 0.120612 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .*.* optimization finished, #iter = 255 nu = 0.047948 obj = -5.971581, rho = 0.279924 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) ...*...................................* optimization finished, #iter = 3881 nu = 0.039871 obj = -6.143643, rho = 0.236080 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) ..*.* optimization finished, #iter = 365 nu = 0.032149 obj = -6.248066, rho = 0.231604 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) ..*.* optimization finished, #iter = 347 nu = 0.025878 obj = -6.253812, rho = 0.242205 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) ..*.* optimization finished, #iter = 347 nu = 0.020308 obj = -6.253812, rho = 0.242205 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) ..*.* optimization finished, #iter = 347 nu = 0.015937 obj = -6.253812, rho = 0.242205 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) ..*.* optimization finished, #iter = 347 nu = 0.012507 obj = -6.253812, rho = 0.242205 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.212031 obj = -1.525021, rho = -0.092390 nSV = 27, nBSV = 19 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 191 nu = 0.195728 obj = -1.760803, rho = -0.014454 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 204 nu = 0.174616 obj = -2.032446, rho = 0.071277 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 133 nu = 0.158665 obj = -2.354026, rho = 0.105924 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.142522 obj = -2.732718, rho = 0.122304 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 164 nu = 0.128878 obj = -3.186230, rho = 0.089144 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 144 nu = 0.121935 obj = -3.702878, rho = -0.035282 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 172 nu = 0.110916 obj = -4.257398, rho = -0.076138 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 71 nu = 0.102424 obj = -4.874472, rho = -0.253025 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 96 nu = 0.091941 obj = -5.542051, rho = -0.127774 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 147 nu = 0.082778 obj = -6.303389, rho = 0.115724 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.075759 obj = -7.087739, rho = 0.352624 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 98 nu = 0.068698 obj = -7.886120, rho = 0.430457 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..*.* optimization finished, #iter = 312 nu = 0.058435 obj = -8.711384, rho = 0.472444 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..* optimization finished, #iter = 289 nu = 0.050894 obj = -9.681282, rho = 0.564444 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..* optimization finished, #iter = 282 nu = 0.044511 obj = -10.757941, rho = 0.624764 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 258 nu = 0.041388 obj = -11.790433, rho = 1.035857 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..*.* optimization finished, #iter = 396 nu = 0.038015 obj = -12.361450, rho = 1.177750 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ...*.* optimization finished, #iter = 419 nu = 0.031670 obj = -12.430334, rho = 1.236265 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) ...*.* optimization finished, #iter = 419 nu = 0.024853 obj = -12.430334, rho = 1.236265 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 77 nu = 0.220679 obj = -1.477566, rho = -0.151240 nSV = 26, nBSV = 18 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.196079 obj = -1.667769, rho = -0.164253 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 78 nu = 0.171958 obj = -1.891137, rho = -0.137675 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 91 nu = 0.153829 obj = -2.145707, rho = -0.142401 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 77 nu = 0.139176 obj = -2.423901, rho = -0.193993 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 79 nu = 0.123663 obj = -2.723708, rho = -0.134675 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 267 nu = 0.110214 obj = -3.044441, rho = -0.088225 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.096488 obj = -3.396855, rho = -0.116668 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 189 nu = 0.084886 obj = -3.796027, rho = -0.068831 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 158 nu = 0.079245 obj = -4.187875, rho = 0.097267 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*..* optimization finished, #iter = 305 nu = 0.070211 obj = -4.492584, rho = 0.091061 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 296 nu = 0.063313 obj = -4.647728, rho = -0.046066 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) ..*...* optimization finished, #iter = 517 nu = 0.050983 obj = -4.673003, rho = -0.076106 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*...* optimization finished, #iter = 517 nu = 0.040010 obj = -4.673003, rho = -0.076106 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*...* optimization finished, #iter = 517 nu = 0.031398 obj = -4.673003, rho = -0.076106 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*...* optimization finished, #iter = 517 nu = 0.024640 obj = -4.673003, rho = -0.076106 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*...* optimization finished, #iter = 517 nu = 0.019336 obj = -4.673003, rho = -0.076106 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*...* optimization finished, #iter = 517 nu = 0.015174 obj = -4.673003, rho = -0.076106 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*...* optimization finished, #iter = 517 nu = 0.011908 obj = -4.673003, rho = -0.076106 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*...* optimization finished, #iter = 517 nu = 0.009345 obj = -4.673003, rho = -0.076106 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 44 nu = 0.174501 obj = -1.115810, rho = -0.223153 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 51 nu = 0.150351 obj = -1.244121, rho = -0.237299 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 70 nu = 0.135200 obj = -1.384613, rho = -0.205026 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 99 nu = 0.118020 obj = -1.528613, rho = -0.173181 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.101349 obj = -1.684887, rho = -0.163061 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.087282 obj = -1.866745, rho = -0.123896 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 196 nu = 0.076990 obj = -2.068578, rho = -0.081841 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.066102 obj = -2.290519, rho = -0.122198 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 86 nu = 0.057969 obj = -2.534209, rho = -0.200775 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 157 nu = 0.051142 obj = -2.789210, rho = -0.286796 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.049185 obj = -2.972339, rho = -0.346406 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ...*...* optimization finished, #iter = 604 nu = 0.041495 obj = -3.024160, rho = -0.361290 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .* optimization finished, #iter = 198 nu = 0.033046 obj = -3.028214, rho = -0.378803 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) .* optimization finished, #iter = 198 nu = 0.025933 obj = -3.028214, rho = -0.378803 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) .* optimization finished, #iter = 198 nu = 0.020351 obj = -3.028214, rho = -0.378803 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) .* optimization finished, #iter = 198 nu = 0.015971 obj = -3.028214, rho = -0.378803 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) .* optimization finished, #iter = 198 nu = 0.012533 obj = -3.028214, rho = -0.378803 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) .* optimization finished, #iter = 198 nu = 0.009836 obj = -3.028214, rho = -0.378803 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) .* optimization finished, #iter = 198 nu = 0.007719 obj = -3.028214, rho = -0.378803 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) .* optimization finished, #iter = 198 nu = 0.006057 obj = -3.028214, rho = -0.378803 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) * optimization finished, #iter = 86 nu = 0.181842 obj = -1.184926, rho = 0.099695 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.161468 obj = -1.325348, rho = 0.117319 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 231 nu = 0.140458 obj = -1.479822, rho = 0.182760 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 43 nu = 0.125620 obj = -1.650791, rho = 0.182197 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 242 nu = 0.113360 obj = -1.811975, rho = 0.078662 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 137 nu = 0.097384 obj = -1.964801, rho = 0.109819 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.083227 obj = -2.127386, rho = 0.144229 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*..* optimization finished, #iter = 379 nu = 0.074435 obj = -2.264803, rho = 0.297067 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..* optimization finished, #iter = 231 nu = 0.062013 obj = -2.381063, rho = 0.357268 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 126 nu = 0.052546 obj = -2.466602, rho = 0.445361 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.043727 obj = -2.511927, rho = 0.490570 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 184 nu = 0.034977 obj = -2.515469, rho = 0.499860 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 184 nu = 0.027449 obj = -2.515469, rho = 0.499860 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 184 nu = 0.021541 obj = -2.515469, rho = 0.499860 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 184 nu = 0.016904 obj = -2.515469, rho = 0.499860 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 184 nu = 0.013266 obj = -2.515469, rho = 0.499860 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 184 nu = 0.010410 obj = -2.515469, rho = 0.499860 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 184 nu = 0.008170 obj = -2.515469, rho = 0.499860 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 184 nu = 0.006411 obj = -2.515469, rho = 0.499860 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 184 nu = 0.005031 obj = -2.515469, rho = 0.499860 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 84 nu = 0.180516 obj = -1.275999, rho = -0.441686 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.164321 obj = -1.469804, rho = -0.421406 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 147 nu = 0.148491 obj = -1.693835, rho = -0.401707 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 216 nu = 0.136049 obj = -1.941339, rho = -0.361465 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 144 nu = 0.119222 obj = -2.224895, rho = -0.328087 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 190 nu = 0.105543 obj = -2.577917, rho = -0.318675 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 63 nu = 0.100000 obj = -2.992834, rho = -0.258160 nSV = 13, nBSV = 8 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 70 nu = 0.090840 obj = -3.416883, rho = -0.286609 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.086555 obj = -3.851212, rho = -0.306558 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.076288 obj = -4.257182, rho = -0.544077 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 205 nu = 0.064927 obj = -4.726438, rho = -0.663601 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.055849 obj = -5.287539, rho = -0.736121 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 88 nu = 0.050274 obj = -5.925320, rho = -0.679422 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 96 nu = 0.046955 obj = -6.506869, rho = -0.389496 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 138 nu = 0.042534 obj = -6.902768, rho = -0.131741 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 143 nu = 0.036888 obj = -6.994703, rho = -0.032627 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 143 nu = 0.028949 obj = -6.994703, rho = -0.032627 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 143 nu = 0.022718 obj = -6.994703, rho = -0.032627 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 143 nu = 0.017828 obj = -6.994703, rho = -0.032627 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 143 nu = 0.013991 obj = -6.994703, rho = -0.032627 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 67 nu = 0.220797 obj = -1.499668, rho = -0.020729 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 85 nu = 0.202274 obj = -1.695928, rho = -0.148150 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 184 nu = 0.175678 obj = -1.904970, rho = -0.153728 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 151 nu = 0.152158 obj = -2.164518, rho = -0.197088 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 88 nu = 0.139988 obj = -2.461836, rho = -0.214092 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.124523 obj = -2.766808, rho = -0.218687 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.112295 obj = -3.111856, rho = -0.282930 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 94 nu = 0.100858 obj = -3.450443, rho = -0.456774 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 139 nu = 0.090100 obj = -3.781462, rho = -0.505528 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.076790 obj = -4.119199, rho = -0.640841 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.068691 obj = -4.434028, rho = -0.838440 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 74 nu = 0.060011 obj = -4.650677, rho = -0.671417 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.051488 obj = -4.719757, rho = -0.403144 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.040406 obj = -4.719757, rho = -0.403144 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.031709 obj = -4.719757, rho = -0.403144 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.024884 obj = -4.719757, rho = -0.403144 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.019528 obj = -4.719757, rho = -0.403144 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.015325 obj = -4.719757, rho = -0.403144 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.012026 obj = -4.719757, rho = -0.403144 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.009438 obj = -4.719757, rho = -0.403144 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 68 nu = 0.159815 obj = -1.016432, rho = 0.054847 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 66 nu = 0.136198 obj = -1.133161, rho = -0.038178 nSV = 20, nBSV = 9 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 38 nu = 0.119576 obj = -1.274283, rho = -0.056043 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 82 nu = 0.108999 obj = -1.417831, rho = -0.052132 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 27 nu = 0.095668 obj = -1.561642, rho = -0.014024 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 45 nu = 0.084800 obj = -1.695510, rho = 0.040156 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 59 nu = 0.075814 obj = -1.816316, rho = 0.052629 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 193 nu = 0.066048 obj = -1.871846, rho = 0.003550 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 135 nu = 0.052638 obj = -1.908706, rho = 0.038684 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 171 nu = 0.042218 obj = -1.946945, rho = 0.093583 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*.* optimization finished, #iter = 300 nu = 0.034528 obj = -1.979231, rho = 0.191749 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 277 nu = 0.027536 obj = -1.980512, rho = 0.232619 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 277 nu = 0.021609 obj = -1.980512, rho = 0.232619 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 277 nu = 0.016958 obj = -1.980512, rho = 0.232619 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 277 nu = 0.013308 obj = -1.980512, rho = 0.232619 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 277 nu = 0.010443 obj = -1.980512, rho = 0.232619 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 277 nu = 0.008196 obj = -1.980512, rho = 0.232619 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 277 nu = 0.006432 obj = -1.980512, rho = 0.232619 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 277 nu = 0.005047 obj = -1.980512, rho = 0.232619 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 277 nu = 0.003961 obj = -1.980512, rho = 0.232619 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 76 nu = 0.187378 obj = -1.262079, rho = -0.443219 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 58 nu = 0.166132 obj = -1.433957, rho = -0.394904 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 42 nu = 0.152062 obj = -1.620995, rho = -0.443949 nSV = 18, nBSV = 13 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 43 nu = 0.137904 obj = -1.813105, rho = -0.602107 nSV = 16, nBSV = 11 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.118846 obj = -2.014323, rho = -0.660553 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*..* optimization finished, #iter = 335 nu = 0.102233 obj = -2.252619, rho = -0.667611 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) ...* optimization finished, #iter = 398 nu = 0.088637 obj = -2.536607, rho = -0.632298 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) ...*...* optimization finished, #iter = 670 nu = 0.081368 obj = -2.842218, rho = -0.764320 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.070479 obj = -3.161160, rho = -0.834076 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*..* optimization finished, #iter = 382 nu = 0.062660 obj = -3.511506, rho = -0.946324 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 272 nu = 0.055734 obj = -3.872155, rho = -1.016990 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.050025 obj = -4.202664, rho = -1.095688 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.044173 obj = -4.456176, rho = -1.135651 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ...*....* optimization finished, #iter = 704 nu = 0.038095 obj = -4.566091, rho = -1.163947 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ......*...........* optimization finished, #iter = 1713 nu = 0.030241 obj = -4.627427, rho = -1.163585 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ...*.* optimization finished, #iter = 471 nu = 0.023913 obj = -4.696407, rho = -1.163393 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ..*.* optimization finished, #iter = 357 nu = 0.019572 obj = -4.729986, rho = -1.092045 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) ..*.* optimization finished, #iter = 357 nu = 0.015359 obj = -4.729986, rho = -1.092045 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) ..*.* optimization finished, #iter = 357 nu = 0.012053 obj = -4.729986, rho = -1.092045 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) ..*.* optimization finished, #iter = 357 nu = 0.009459 obj = -4.729986, rho = -1.092045 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.243575 obj = -1.564573, rho = -0.159924 nSV = 28, nBSV = 21 Total nSV = 28 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) .**.* optimization finished, #iter = 164 nu = 0.212766 obj = -1.739348, rho = -0.149716 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) .*.* optimization finished, #iter = 214 nu = 0.185619 obj = -1.938045, rho = -0.204148 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.163282 obj = -2.154543, rho = -0.189200 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 235 nu = 0.139847 obj = -2.393214, rho = -0.185694 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) ..*.* optimization finished, #iter = 320 nu = 0.120064 obj = -2.691634, rho = -0.170078 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) ..*..* optimization finished, #iter = 419 nu = 0.103585 obj = -3.057929, rho = -0.155524 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) .*.* optimization finished, #iter = 237 nu = 0.091656 obj = -3.507739, rho = -0.115977 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) ..* optimization finished, #iter = 259 nu = 0.086996 obj = -3.996640, rho = -0.304971 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 185 nu = 0.074859 obj = -4.522654, rho = -0.346008 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*..* optimization finished, #iter = 304 nu = 0.068382 obj = -5.126624, rho = -0.584439 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .....*....* optimization finished, #iter = 910 nu = 0.061128 obj = -5.771984, rho = -0.688247 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .....*....* optimization finished, #iter = 937 nu = 0.052834 obj = -6.507667, rho = -0.652469 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .....* optimization finished, #iter = 549 nu = 0.047913 obj = -7.348290, rho = -0.711412 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) ......*...* optimization finished, #iter = 964 nu = 0.044051 obj = -8.172898, rho = -0.851280 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) ...*.......* optimization finished, #iter = 1074 nu = 0.039821 obj = -8.865625, rho = -0.979479 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ....*..* optimization finished, #iter = 645 nu = 0.035930 obj = -9.401740, rho = -0.886626 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ........*.....* optimization finished, #iter = 1327 nu = 0.030884 obj = -9.510756, rho = -0.820033 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ........*.....* optimization finished, #iter = 1327 nu = 0.024236 obj = -9.510756, rho = -0.820033 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ........*.....* optimization finished, #iter = 1327 nu = 0.019020 obj = -9.510756, rho = -0.820033 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 154 nu = 0.148315 obj = -0.937515, rho = -0.068807 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 165 nu = 0.126306 obj = -1.042628, rho = -0.042196 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 42 nu = 0.111051 obj = -1.167349, rho = -0.065886 nSV = 13, nBSV = 8 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.100879 obj = -1.288260, rho = -0.138168 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 64 nu = 0.086925 obj = -1.411086, rho = -0.203441 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 83 nu = 0.074910 obj = -1.544381, rho = -0.187613 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 188 nu = 0.064041 obj = -1.689396, rho = -0.164526 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 163 nu = 0.060188 obj = -1.815667, rho = -0.138134 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 198 nu = 0.050782 obj = -1.870945, rho = -0.024607 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 201 nu = 0.041264 obj = -1.919752, rho = 0.100415 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.034242 obj = -1.948772, rho = 0.190154 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 178 nu = 0.027104 obj = -1.949297, rho = 0.203478 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 178 nu = 0.021270 obj = -1.949297, rho = 0.203478 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 178 nu = 0.016692 obj = -1.949297, rho = 0.203478 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 178 nu = 0.013099 obj = -1.949297, rho = 0.203478 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 178 nu = 0.010280 obj = -1.949297, rho = 0.203478 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 178 nu = 0.008067 obj = -1.949297, rho = 0.203478 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 178 nu = 0.006331 obj = -1.949297, rho = 0.203478 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 178 nu = 0.004968 obj = -1.949297, rho = 0.203478 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 178 nu = 0.003899 obj = -1.949297, rho = 0.203478 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.197013 obj = -1.297848, rho = -0.427257 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 79 nu = 0.175097 obj = -1.459277, rho = -0.412719 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.153390 obj = -1.638704, rho = -0.340108 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 154 nu = 0.135452 obj = -1.835612, rho = -0.282533 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 174 nu = 0.120171 obj = -2.054525, rho = -0.154350 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 150 nu = 0.103303 obj = -2.308470, rho = -0.180697 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 131 nu = 0.089743 obj = -2.613288, rho = -0.137126 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 110 nu = 0.077833 obj = -2.990789, rho = -0.112013 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 88 nu = 0.068901 obj = -3.456995, rho = -0.051915 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 68 nu = 0.062961 obj = -4.015191, rho = 0.081258 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 53 nu = 0.061320 obj = -4.594827, rho = 0.162930 nSV = 11, nBSV = 5 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.058464 obj = -5.085220, rho = 0.248962 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 141 nu = 0.053066 obj = -5.451434, rho = 0.093228 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 120 nu = 0.047134 obj = -5.639513, rho = 0.121560 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 188 nu = 0.037928 obj = -5.644992, rho = 0.134289 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 188 nu = 0.029764 obj = -5.644992, rho = 0.134289 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 188 nu = 0.023358 obj = -5.644992, rho = 0.134289 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 188 nu = 0.018330 obj = -5.644992, rho = 0.134289 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 188 nu = 0.014385 obj = -5.644992, rho = 0.134289 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 188 nu = 0.011289 obj = -5.644992, rho = 0.134289 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 151 nu = 0.184027 obj = -1.178668, rho = 0.190593 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.156592 obj = -1.318947, rho = 0.168543 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 59 nu = 0.144224 obj = -1.472244, rho = 0.271843 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*..* optimization finished, #iter = 385 nu = 0.123598 obj = -1.627644, rho = 0.325998 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*...* optimization finished, #iter = 480 nu = 0.105753 obj = -1.814024, rho = 0.330430 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 97 nu = 0.092839 obj = -2.029087, rho = 0.304962 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 98 nu = 0.083132 obj = -2.252279, rho = 0.278275 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 130 nu = 0.071769 obj = -2.499875, rho = 0.443674 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.064143 obj = -2.768518, rho = 0.410370 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 149 nu = 0.055818 obj = -3.023897, rho = 0.371599 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 61 nu = 0.049185 obj = -3.297223, rho = 0.339541 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 63 nu = 0.043520 obj = -3.514809, rho = 0.321340 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 138 nu = 0.039453 obj = -3.621542, rho = 0.259623 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.031006 obj = -3.621557, rho = 0.259208 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.024333 obj = -3.621557, rho = 0.259208 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.019095 obj = -3.621557, rho = 0.259208 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.014985 obj = -3.621557, rho = 0.259208 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.011760 obj = -3.621557, rho = 0.259208 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.009229 obj = -3.621557, rho = 0.259208 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.007242 obj = -3.621557, rho = 0.259208 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.190478 obj = -1.178587, rho = 0.015908 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 163 nu = 0.173386 obj = -1.278123, rho = -0.010953 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 120 nu = 0.150475 obj = -1.351251, rho = 0.005840 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*..........* optimization finished, #iter = 1207 nu = 0.125142 obj = -1.406836, rho = 0.006962 nSV = 18, nBSV = 7 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ...**..* optimization finished, #iter = 503 nu = 0.101993 obj = -1.462905, rho = 0.012074 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .....*..* optimization finished, #iter = 705 nu = 0.082029 obj = -1.527399, rho = 0.009506 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ...*....* optimization finished, #iter = 716 nu = 0.067092 obj = -1.600683, rho = 0.004381 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*...*.* optimization finished, #iter = 539 nu = 0.055563 obj = -1.684590, rho = 0.017806 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *...........* optimization finished, #iter = 1182 nu = 0.047875 obj = -1.748100, rho = 0.048093 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 130 nu = 0.039186 obj = -1.776288, rho = 0.072288 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 189 nu = 0.031509 obj = -1.778468, rho = 0.090349 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 189 nu = 0.024727 obj = -1.778468, rho = 0.090349 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 189 nu = 0.019405 obj = -1.778468, rho = 0.090349 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 189 nu = 0.015228 obj = -1.778468, rho = 0.090349 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 189 nu = 0.011950 obj = -1.778468, rho = 0.090349 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 189 nu = 0.009378 obj = -1.778468, rho = 0.090349 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 189 nu = 0.007360 obj = -1.778468, rho = 0.090349 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 189 nu = 0.005776 obj = -1.778468, rho = 0.090349 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 189 nu = 0.004532 obj = -1.778468, rho = 0.090349 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 189 nu = 0.003557 obj = -1.778468, rho = 0.090349 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.203295 obj = -1.293943, rho = -0.395186 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 47 nu = 0.179653 obj = -1.432080, rho = -0.475210 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 61 nu = 0.156414 obj = -1.580718, rho = -0.489116 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 203 nu = 0.137547 obj = -1.727494, rho = -0.556026 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 96 nu = 0.117453 obj = -1.883381, rho = -0.561845 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 78 nu = 0.105176 obj = -2.039246, rho = -0.578077 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 162 nu = 0.089151 obj = -2.157485, rho = -0.585970 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 140 nu = 0.075267 obj = -2.272718, rho = -0.556663 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 191 nu = 0.063450 obj = -2.370541, rho = -0.519840 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 142 nu = 0.053426 obj = -2.426089, rho = -0.433952 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 194 nu = 0.043125 obj = -2.433674, rho = -0.385446 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 194 nu = 0.033843 obj = -2.433674, rho = -0.385446 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 194 nu = 0.026558 obj = -2.433674, rho = -0.385446 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 194 nu = 0.020842 obj = -2.433674, rho = -0.385446 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 194 nu = 0.016356 obj = -2.433674, rho = -0.385446 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 194 nu = 0.012835 obj = -2.433674, rho = -0.385446 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 194 nu = 0.010073 obj = -2.433674, rho = -0.385446 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 194 nu = 0.007905 obj = -2.433674, rho = -0.385446 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 194 nu = 0.006203 obj = -2.433674, rho = -0.385446 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 194 nu = 0.004868 obj = -2.433674, rho = -0.385446 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.200253 obj = -1.373717, rho = -0.479874 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 63 nu = 0.177120 obj = -1.571469, rho = -0.488630 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.162520 obj = -1.792330, rho = -0.456981 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) .*.* optimization finished, #iter = 205 nu = 0.146332 obj = -2.023227, rho = -0.434914 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) ..* optimization finished, #iter = 258 nu = 0.126279 obj = -2.298693, rho = -0.442833 nSV = 19, nBSV = 8 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.109556 obj = -2.646153, rho = -0.454150 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 120 nu = 0.097057 obj = -3.085923, rho = -0.509514 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 131 nu = 0.089277 obj = -3.615275, rho = -0.586314 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 214 nu = 0.081059 obj = -4.242174, rho = -0.426266 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98.8% (988/1000) (classification) .*.* optimization finished, #iter = 201 nu = 0.073652 obj = -5.012322, rho = -0.480779 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 139 nu = 0.071368 obj = -5.895568, rho = -0.441326 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 176 nu = 0.068258 obj = -6.811658, rho = -0.484655 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.062352 obj = -7.775812, rho = -0.552776 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .* optimization finished, #iter = 154 nu = 0.058508 obj = -8.756660, rho = -0.654911 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.056470 obj = -9.556029, rho = -1.015262 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 198 nu = 0.049282 obj = -9.933609, rho = -1.364870 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 249 nu = 0.041700 obj = -10.079225, rho = -1.698435 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 249 nu = 0.032725 obj = -10.079225, rho = -1.698435 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 249 nu = 0.025681 obj = -10.079225, rho = -1.698435 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 249 nu = 0.020153 obj = -10.079225, rho = -1.698435 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 95 nu = 0.182631 obj = -1.181379, rho = -0.122051 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 59 nu = 0.159554 obj = -1.321424, rho = -0.108448 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 67 nu = 0.141625 obj = -1.474160, rho = -0.219519 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 89 nu = 0.124059 obj = -1.638807, rho = -0.198235 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 69 nu = 0.108604 obj = -1.821748, rho = -0.137648 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 64 nu = 0.098615 obj = -2.001978, rho = -0.086771 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*......* optimization finished, #iter = 780 nu = 0.083821 obj = -2.166198, rho = -0.063766 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 87 nu = 0.070332 obj = -2.367395, rho = -0.032957 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.060482 obj = -2.596748, rho = 0.048567 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 187 nu = 0.051639 obj = -2.852389, rho = -0.028628 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 221 nu = 0.043678 obj = -3.150283, rho = -0.070405 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 187 nu = 0.037317 obj = -3.523627, rho = -0.075909 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*..* optimization finished, #iter = 407 nu = 0.032707 obj = -3.965930, rho = -0.139402 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.030498 obj = -4.425048, rho = -0.278833 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 145 nu = 0.029142 obj = -4.751602, rho = -0.461545 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 191 nu = 0.025331 obj = -4.804874, rho = -0.575607 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 191 nu = 0.019879 obj = -4.804874, rho = -0.575607 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 191 nu = 0.015600 obj = -4.804874, rho = -0.575607 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 191 nu = 0.012242 obj = -4.804874, rho = -0.575607 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 191 nu = 0.009607 obj = -4.804874, rho = -0.575607 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 78 nu = 0.176598 obj = -1.175847, rho = -0.084562 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.160997 obj = -1.315176, rho = -0.066068 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 130 nu = 0.139625 obj = -1.469577, rho = 0.010023 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *...* optimization finished, #iter = 344 nu = 0.123566 obj = -1.634933, rho = 0.031904 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*....* optimization finished, #iter = 550 nu = 0.107034 obj = -1.819186, rho = -0.034995 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 137 nu = 0.092696 obj = -2.039893, rho = -0.032636 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 224 nu = 0.081145 obj = -2.286663, rho = -0.029282 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 135 nu = 0.071331 obj = -2.571522, rho = -0.038741 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 93 nu = 0.064186 obj = -2.891298, rho = -0.068297 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 83 nu = 0.056078 obj = -3.234884, rho = -0.193710 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 90 nu = 0.053537 obj = -3.555806, rho = -0.423157 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.047473 obj = -3.742483, rho = -0.632323 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 197 nu = 0.038777 obj = -3.902757, rho = -0.659876 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 142 nu = 0.032965 obj = -4.053142, rho = -0.864513 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 233 nu = 0.027382 obj = -4.076059, rho = -0.989724 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 233 nu = 0.021488 obj = -4.076059, rho = -0.989724 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 233 nu = 0.016863 obj = -4.076059, rho = -0.989724 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 233 nu = 0.013234 obj = -4.076059, rho = -0.989724 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 233 nu = 0.010385 obj = -4.076059, rho = -0.989724 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 233 nu = 0.008150 obj = -4.076059, rho = -0.989724 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 86 nu = 0.169703 obj = -1.089958, rho = -0.365678 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 86 nu = 0.149713 obj = -1.209838, rho = -0.394637 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.130979 obj = -1.336639, rho = -0.391642 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 224 nu = 0.111951 obj = -1.481066, rho = -0.387950 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 130 nu = 0.096607 obj = -1.647650, rho = -0.388219 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 62 nu = 0.087044 obj = -1.828453, rho = -0.583074 nSV = 11, nBSV = 5 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 67 nu = 0.076147 obj = -2.008767, rho = -0.764296 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 75 nu = 0.067036 obj = -2.190460, rho = -1.008304 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 186 nu = 0.058434 obj = -2.350843, rho = -1.216228 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .*.* optimization finished, #iter = 268 nu = 0.052042 obj = -2.477002, rho = -1.533892 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) .* optimization finished, #iter = 143 nu = 0.044148 obj = -2.522112, rho = -1.783883 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.3% (943/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.035126 obj = -2.526164, rho = -1.838528 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.3% (943/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.027565 obj = -2.526164, rho = -1.838528 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.3% (943/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.021632 obj = -2.526164, rho = -1.838528 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.3% (943/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.016976 obj = -2.526164, rho = -1.838528 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.3% (943/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.013322 obj = -2.526164, rho = -1.838528 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.3% (943/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.010455 obj = -2.526164, rho = -1.838528 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.3% (943/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.008204 obj = -2.526164, rho = -1.838528 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.3% (943/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.006439 obj = -2.526164, rho = -1.838528 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.3% (943/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.005053 obj = -2.526164, rho = -1.838528 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.3% (943/1000) (classification) * optimization finished, #iter = 61 nu = 0.167895 obj = -1.123663, rho = -0.072216 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 41 nu = 0.147707 obj = -1.273636, rho = -0.049848 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 99.2% (992/1000) (classification) * optimization finished, #iter = 48 nu = 0.132132 obj = -1.443132, rho = -0.040115 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.117122 obj = -1.634238, rho = 0.020507 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 72 nu = 0.103987 obj = -1.855719, rho = 0.092093 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 53 nu = 0.095696 obj = -2.097281, rho = 0.248274 nSV = 11, nBSV = 6 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 95 nu = 0.088266 obj = -2.319711, rho = 0.485445 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.080027 obj = -2.501203, rho = 0.733414 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 171 nu = 0.067879 obj = -2.638878, rho = 0.778251 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 174 nu = 0.055615 obj = -2.788540, rho = 0.798199 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 154 nu = 0.046368 obj = -2.949164, rho = 0.789259 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*..*......* optimization finished, #iter = 998 nu = 0.039006 obj = -3.103302, rho = 0.766025 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ......*.* optimization finished, #iter = 745 nu = 0.033170 obj = -3.227809, rho = 0.712397 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*..* optimization finished, #iter = 359 nu = 0.027415 obj = -3.276023, rho = 0.695247 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*.* optimization finished, #iter = 337 nu = 0.022060 obj = -3.283065, rho = 0.669061 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*.* optimization finished, #iter = 337 nu = 0.017312 obj = -3.283065, rho = 0.669061 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*.* optimization finished, #iter = 337 nu = 0.013586 obj = -3.283065, rho = 0.669061 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*.* optimization finished, #iter = 337 nu = 0.010661 obj = -3.283065, rho = 0.669061 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*.* optimization finished, #iter = 337 nu = 0.008367 obj = -3.283065, rho = 0.669061 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) ..*.* optimization finished, #iter = 337 nu = 0.006566 obj = -3.283065, rho = 0.669061 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 47 nu = 0.172970 obj = -1.087608, rho = -0.169844 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 69 nu = 0.149808 obj = -1.200178, rho = -0.227517 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.130692 obj = -1.321348, rho = -0.313826 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.113041 obj = -1.457411, rho = -0.287106 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *..* optimization finished, #iter = 229 nu = 0.101713 obj = -1.588542, rho = -0.203644 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.090165 obj = -1.689437, rho = -0.103687 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 143 nu = 0.074050 obj = -1.774869, rho = -0.109319 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 223 nu = 0.060828 obj = -1.870852, rho = -0.164441 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 72 nu = 0.050948 obj = -1.977098, rho = -0.235726 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..*...* optimization finished, #iter = 536 nu = 0.045406 obj = -2.042308, rho = -0.375899 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .....*....* optimization finished, #iter = 917 nu = 0.036245 obj = -2.046039, rho = -0.378119 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .....*....* optimization finished, #iter = 917 nu = 0.028444 obj = -2.046039, rho = -0.378119 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .....*....* optimization finished, #iter = 917 nu = 0.022322 obj = -2.046039, rho = -0.378119 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .....*....* optimization finished, #iter = 917 nu = 0.017517 obj = -2.046039, rho = -0.378119 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .....*....* optimization finished, #iter = 917 nu = 0.013747 obj = -2.046039, rho = -0.378119 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .....*....* optimization finished, #iter = 917 nu = 0.010788 obj = -2.046039, rho = -0.378119 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .....*....* optimization finished, #iter = 917 nu = 0.008466 obj = -2.046039, rho = -0.378119 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .....*....* optimization finished, #iter = 917 nu = 0.006644 obj = -2.046039, rho = -0.378119 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .....*....* optimization finished, #iter = 917 nu = 0.005214 obj = -2.046039, rho = -0.378119 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .....*....* optimization finished, #iter = 917 nu = 0.004092 obj = -2.046039, rho = -0.378119 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 140 nu = 0.209314 obj = -1.438972, rho = 0.020031 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 78 nu = 0.184725 obj = -1.650280, rho = 0.064033 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *...* optimization finished, #iter = 391 nu = 0.165728 obj = -1.889723, rho = 0.163972 nSV = 23, nBSV = 13 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 98 nu = 0.148433 obj = -2.181225, rho = 0.128260 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 89 nu = 0.136834 obj = -2.506357, rho = 0.120015 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 99 nu = 0.125043 obj = -2.859309, rho = 0.179744 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 172 nu = 0.111958 obj = -3.250313, rho = 0.226463 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 154 nu = 0.100718 obj = -3.682690, rho = 0.324548 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ....*.....* optimization finished, #iter = 949 nu = 0.089978 obj = -4.152665, rho = 0.492247 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.......* optimization finished, #iter = 897 nu = 0.082753 obj = -4.651912, rho = 0.596264 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) ..* optimization finished, #iter = 297 nu = 0.075985 obj = -5.069627, rho = 0.736686 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) ..* optimization finished, #iter = 224 nu = 0.064089 obj = -5.457714, rho = 0.803263 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 160 nu = 0.057170 obj = -5.838081, rho = 0.807619 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 171 nu = 0.049966 obj = -5.994527, rho = 0.843321 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*.* optimization finished, #iter = 230 nu = 0.040373 obj = -6.009775, rho = 0.907635 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*.* optimization finished, #iter = 230 nu = 0.031683 obj = -6.009775, rho = 0.907635 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*.* optimization finished, #iter = 230 nu = 0.024864 obj = -6.009775, rho = 0.907635 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*.* optimization finished, #iter = 230 nu = 0.019512 obj = -6.009775, rho = 0.907635 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*.* optimization finished, #iter = 230 nu = 0.015312 obj = -6.009775, rho = 0.907635 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*.* optimization finished, #iter = 230 nu = 0.012016 obj = -6.009775, rho = 0.907635 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) *.* optimization finished, #iter = 194 nu = 0.199819 obj = -1.249303, rho = -0.163500 nSV = 26, nBSV = 16 Total nSV = 26 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 97 nu = 0.171026 obj = -1.380511, rho = -0.163737 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.148614 obj = -1.528857, rho = -0.169608 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ...*.* optimization finished, #iter = 458 nu = 0.129746 obj = -1.685453, rho = -0.146636 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*................*..* optimization finished, #iter = 1855 nu = 0.110257 obj = -1.869477, rho = -0.132790 nSV = 18, nBSV = 7 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) ...* optimization finished, #iter = 359 nu = 0.095628 obj = -2.087914, rho = -0.099861 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 205 nu = 0.083693 obj = -2.338817, rho = -0.088958 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 84 nu = 0.074763 obj = -2.606823, rho = -0.090444 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 63 nu = 0.066980 obj = -2.888778, rho = -0.237493 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 176 nu = 0.059247 obj = -3.139895, rho = -0.440168 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.050208 obj = -3.412757, rho = -0.490149 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.043252 obj = -3.687843, rho = -0.601658 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.039711 obj = -3.902587, rho = -0.536100 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 158 nu = 0.033722 obj = -3.938339, rho = -0.585109 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 158 nu = 0.026464 obj = -3.938339, rho = -0.585109 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 158 nu = 0.020768 obj = -3.938339, rho = -0.585109 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 158 nu = 0.016298 obj = -3.938339, rho = -0.585109 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 158 nu = 0.012790 obj = -3.938339, rho = -0.585109 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 158 nu = 0.010037 obj = -3.938339, rho = -0.585109 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .* optimization finished, #iter = 158 nu = 0.007877 obj = -3.938339, rho = -0.585109 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 74 nu = 0.221734 obj = -1.410660, rho = -0.572475 nSV = 27, nBSV = 20 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .**.* optimization finished, #iter = 171 nu = 0.194873 obj = -1.556080, rho = -0.614730 nSV = 25, nBSV = 15 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 207 nu = 0.167654 obj = -1.721264, rho = -0.631443 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*..* optimization finished, #iter = 311 nu = 0.144940 obj = -1.906537, rho = -0.632168 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .*.* optimization finished, #iter = 231 nu = 0.127175 obj = -2.101794, rho = -0.647016 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 181 nu = 0.112493 obj = -2.314051, rho = -0.740314 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*..* optimization finished, #iter = 385 nu = 0.097776 obj = -2.512461, rho = -0.856371 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) ...* optimization finished, #iter = 395 nu = 0.082318 obj = -2.731917, rho = -0.945148 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) ...*.* optimization finished, #iter = 461 nu = 0.069005 obj = -2.992815, rho = -0.965666 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) ..*.* optimization finished, #iter = 363 nu = 0.057904 obj = -3.316762, rho = -0.956536 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) . WARNING: using -h 0 may be faster * optimization finished, #iter = 153 nu = 0.050887 obj = -3.708611, rho = -0.921658 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..*..* optimization finished, #iter = 419 nu = 0.045403 obj = -4.113790, rho = -0.853200 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*..* optimization finished, #iter = 494 nu = 0.038189 obj = -4.578415, rho = -0.855667 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*..* optimization finished, #iter = 412 nu = 0.033371 obj = -5.163685, rho = -0.879470 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..* optimization finished, #iter = 278 nu = 0.030770 obj = -5.776758, rho = -0.963863 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..*.* optimization finished, #iter = 328 nu = 0.029646 obj = -6.237641, rho = -1.084670 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) ....*.....* optimization finished, #iter = 957 nu = 0.026229 obj = -6.337372, rho = -1.118443 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) ....*.....* optimization finished, #iter = 957 nu = 0.020583 obj = -6.337372, rho = -1.118443 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) ....*.....* optimization finished, #iter = 957 nu = 0.016153 obj = -6.337372, rho = -1.118443 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) ....*.....* optimization finished, #iter = 957 nu = 0.012676 obj = -6.337372, rho = -1.118443 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 42 nu = 0.190467 obj = -1.406639, rho = -0.125803 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 37 nu = 0.179343 obj = -1.641801, rho = -0.060048 nSV = 20, nBSV = 16 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.163394 obj = -1.899306, rho = -0.022257 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 125 nu = 0.145717 obj = -2.205266, rho = -0.048997 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 148 nu = 0.133559 obj = -2.571040, rho = -0.147962 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 186 nu = 0.120925 obj = -2.993363, rho = -0.188613 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 93 nu = 0.108390 obj = -3.513528, rho = -0.151233 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 40 nu = 0.100305 obj = -4.147163, rho = -0.107829 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 92 nu = 0.096671 obj = -4.856444, rho = 0.130645 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) ..*....* optimization finished, #iter = 637 nu = 0.089431 obj = -5.605207, rho = 0.324182 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*...* optimization finished, #iter = 515 nu = 0.079060 obj = -6.493604, rho = 0.353786 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..*..* optimization finished, #iter = 491 nu = 0.075239 obj = -7.484520, rho = 0.246394 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 194 nu = 0.070207 obj = -8.458156, rho = -0.017286 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 189 nu = 0.060404 obj = -9.549853, rho = 0.002763 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 150 nu = 0.052708 obj = -10.889378, rho = 0.028444 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 231 nu = 0.046610 obj = -12.513157, rho = 0.059657 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 284 nu = 0.041640 obj = -14.469592, rho = 0.096087 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..*.* optimization finished, #iter = 318 nu = 0.039307 obj = -16.694033, rho = 0.252230 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..* optimization finished, #iter = 251 nu = 0.038031 obj = -18.793460, rho = 0.485273 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 243 nu = 0.037296 obj = -20.185982, rho = 0.767261 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 74 nu = 0.210149 obj = -1.350768, rho = -0.309738 nSV = 26, nBSV = 17 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 74 nu = 0.187508 obj = -1.499048, rho = -0.393400 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 165 nu = 0.160722 obj = -1.657021, rho = -0.482004 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 153 nu = 0.139893 obj = -1.839493, rho = -0.476113 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.124182 obj = -2.036051, rho = -0.485949 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 162 nu = 0.108667 obj = -2.224399, rho = -0.452568 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 188 nu = 0.091772 obj = -2.436175, rho = -0.431730 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *..* optimization finished, #iter = 246 nu = 0.077633 obj = -2.689812, rho = -0.462605 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 225 nu = 0.069056 obj = -2.982910, rho = -0.512775 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*...* optimization finished, #iter = 446 nu = 0.062236 obj = -3.226771, rho = -0.573489 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..*.......* optimization finished, #iter = 925 nu = 0.051129 obj = -3.484825, rho = -0.573769 nSV = 13, nBSV = 1 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .....*.....* optimization finished, #iter = 1053 nu = 0.042407 obj = -3.813347, rho = -0.573625 nSV = 13, nBSV = 1 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..........*..* optimization finished, #iter = 1284 nu = 0.035566 obj = -4.231926, rho = -0.573874 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 220 nu = 0.033353 obj = -4.670952, rho = -0.641218 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 188 nu = 0.031924 obj = -4.920226, rho = -0.729683 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 186 nu = 0.025991 obj = -4.927762, rho = -0.748218 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 186 nu = 0.020397 obj = -4.927762, rho = -0.748218 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 186 nu = 0.016006 obj = -4.927762, rho = -0.748218 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 186 nu = 0.012561 obj = -4.927762, rho = -0.748218 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 186 nu = 0.009857 obj = -4.927762, rho = -0.748218 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 48 nu = 0.220962 obj = -1.479445, rho = -0.073303 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 154 nu = 0.191508 obj = -1.682394, rho = -0.094544 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 52 nu = 0.170763 obj = -1.928123, rho = -0.104912 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*...* optimization finished, #iter = 481 nu = 0.154788 obj = -2.199628, rho = -0.139417 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*...* optimization finished, #iter = 412 nu = 0.135044 obj = -2.523711, rho = -0.146587 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 147 nu = 0.122073 obj = -2.915834, rho = -0.095653 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 95 nu = 0.108849 obj = -3.379554, rho = -0.162487 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.099402 obj = -3.923981, rho = -0.211703 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 75 nu = 0.089878 obj = -4.565930, rho = -0.194853 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.081341 obj = -5.323854, rho = -0.175129 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 91 nu = 0.073310 obj = -6.253556, rho = -0.196240 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 90 nu = 0.068911 obj = -7.365003, rho = -0.204356 nSV = 11, nBSV = 5 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 153 nu = 0.065059 obj = -8.565792, rho = -0.293748 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 189 nu = 0.060023 obj = -9.902376, rho = -0.298885 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 99 nu = 0.057190 obj = -11.324065, rho = -0.344852 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.055885 obj = -12.492547, rho = -0.502629 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) .* optimization finished, #iter = 166 nu = 0.050323 obj = -13.145082, rho = -0.482033 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.043400 obj = -13.367439, rho = -0.362702 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.034059 obj = -13.367439, rho = -0.362702 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) .*.* optimization finished, #iter = 202 nu = 0.026728 obj = -13.367439, rho = -0.362702 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.4% (964/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.176350 obj = -1.061884, rho = -0.364706 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 179 nu = 0.150997 obj = -1.147348, rho = -0.402639 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.127330 obj = -1.240944, rho = -0.401918 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 64 nu = 0.109325 obj = -1.345386, rho = -0.433449 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.093901 obj = -1.450040, rho = -0.398047 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 80 nu = 0.081654 obj = -1.545390, rho = -0.320662 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 80 nu = 0.067760 obj = -1.632251, rho = -0.331904 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.057850 obj = -1.715718, rho = -0.392408 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.049554 obj = -1.772568, rho = -0.525247 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.040094 obj = -1.775843, rho = -0.579470 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.031464 obj = -1.775843, rho = -0.579470 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.024692 obj = -1.775843, rho = -0.579470 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.019377 obj = -1.775843, rho = -0.579470 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.015207 obj = -1.775843, rho = -0.579470 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.011933 obj = -1.775843, rho = -0.579470 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.009365 obj = -1.775843, rho = -0.579470 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.007349 obj = -1.775843, rho = -0.579470 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.005767 obj = -1.775843, rho = -0.579470 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.004526 obj = -1.775843, rho = -0.579470 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 137 nu = 0.003552 obj = -1.775843, rho = -0.579470 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.194463 obj = -1.328176, rho = -0.138099 nSV = 25, nBSV = 16 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 161 nu = 0.173291 obj = -1.513880, rho = -0.160933 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 130 nu = 0.153419 obj = -1.730653, rho = -0.155642 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 96 nu = 0.137542 obj = -1.983430, rho = -0.234110 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.120501 obj = -2.286465, rho = -0.289233 nSV = 18, nBSV = 8 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 79 nu = 0.107533 obj = -2.662338, rho = -0.380173 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 37 nu = 0.102087 obj = -3.098036, rho = -0.519192 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 57 nu = 0.098561 obj = -3.527486, rho = -0.680713 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) ..*.* optimization finished, #iter = 326 nu = 0.086865 obj = -3.938875, rho = -0.762275 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..*..* optimization finished, #iter = 420 nu = 0.075557 obj = -4.429320, rho = -0.877354 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 162 nu = 0.068889 obj = -4.947318, rho = -1.153021 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 184 nu = 0.060179 obj = -5.473157, rho = -1.366953 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 148 nu = 0.052787 obj = -6.076103, rho = -1.471454 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..*.* optimization finished, #iter = 383 nu = 0.049684 obj = -6.584908, rho = -1.811501 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) .*.* optimization finished, #iter = 237 nu = 0.045260 obj = -6.804272, rho = -2.039588 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) .*.* optimization finished, #iter = 250 nu = 0.035929 obj = -6.813063, rho = -2.141435 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .*..................* optimization finished, #iter = 1933 nu = 0.028195 obj = -6.813035, rho = -2.143515 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .*..................* optimization finished, #iter = 1933 nu = 0.022126 obj = -6.813035, rho = -2.143515 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .*..................* optimization finished, #iter = 1933 nu = 0.017364 obj = -6.813035, rho = -2.143515 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) .*..................* optimization finished, #iter = 1933 nu = 0.013626 obj = -6.813035, rho = -2.143515 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.6% (956/1000) (classification) * optimization finished, #iter = 49 nu = 0.228696 obj = -1.513454, rho = -0.284413 nSV = 28, nBSV = 20 Total nSV = 28 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 43 nu = 0.203849 obj = -1.699973, rho = -0.252628 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.176665 obj = -1.910746, rho = -0.268489 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 57 nu = 0.157320 obj = -2.158776, rho = -0.206670 nSV = 19, nBSV = 13 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 60 nu = 0.137686 obj = -2.433632, rho = -0.212598 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 68 nu = 0.121326 obj = -2.764637, rho = -0.183097 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.107745 obj = -3.140217, rho = -0.137597 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 179 nu = 0.098384 obj = -3.557307, rho = -0.103400 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 229 nu = 0.088639 obj = -3.977051, rho = -0.074794 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.......* optimization finished, #iter = 836 nu = 0.077643 obj = -4.438432, rho = -0.112930 nSV = 15, nBSV = 4 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 88 nu = 0.069836 obj = -4.946846, rho = -0.242778 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 238 nu = 0.064512 obj = -5.368056, rho = -0.450812 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.054357 obj = -5.757240, rho = -0.466721 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) ..*.* optimization finished, #iter = 338 nu = 0.048959 obj = -6.017037, rho = -0.554465 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .*.* optimization finished, #iter = 269 nu = 0.040875 obj = -6.083092, rho = -0.586678 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*.* optimization finished, #iter = 269 nu = 0.032077 obj = -6.083092, rho = -0.586678 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*.* optimization finished, #iter = 269 nu = 0.025173 obj = -6.083092, rho = -0.586678 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*.* optimization finished, #iter = 269 nu = 0.019755 obj = -6.083092, rho = -0.586678 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*.* optimization finished, #iter = 269 nu = 0.015503 obj = -6.083092, rho = -0.586678 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*.* optimization finished, #iter = 269 nu = 0.012166 obj = -6.083092, rho = -0.586678 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) * optimization finished, #iter = 81 nu = 0.212497 obj = -1.552656, rho = -0.150011 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.191801 obj = -1.812002, rho = -0.131817 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 97% (97/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 56 nu = 0.176472 obj = -2.126083, rho = -0.087354 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 93 nu = 0.164526 obj = -2.482377, rho = 0.041198 nSV = 22, nBSV = 12 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 128 nu = 0.151867 obj = -2.880105, rho = 0.136795 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 88 nu = 0.137779 obj = -3.345939, rho = 0.144610 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 66 nu = 0.127259 obj = -3.867816, rho = 0.147038 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*..* optimization finished, #iter = 350 nu = 0.112641 obj = -4.474154, rho = 0.153262 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) ......*..* optimization finished, #iter = 836 nu = 0.099634 obj = -5.240566, rho = 0.166220 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .**..............* optimization finished, #iter = 1573 nu = 0.089290 obj = -6.214214, rho = 0.205513 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) .**.* optimization finished, #iter = 189 nu = 0.082546 obj = -7.429229, rho = 0.083713 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98.6% (986/1000) (classification) .*.* optimization finished, #iter = 206 nu = 0.078551 obj = -8.886819, rho = 0.013964 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.......* optimization finished, #iter = 859 nu = 0.074511 obj = -10.568259, rho = -0.172891 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..*.* optimization finished, #iter = 392 nu = 0.069782 obj = -12.543097, rho = -0.500090 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 96.5% (965/1000) (classification) ..*.* optimization finished, #iter = 302 nu = 0.066183 obj = -14.812930, rho = -0.970344 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) .*.* optimization finished, #iter = 239 nu = 0.063830 obj = -17.290521, rho = -1.549896 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 95.7% (957/1000) (classification) ..*.* optimization finished, #iter = 321 nu = 0.060081 obj = -19.765428, rho = -2.093275 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 95% (950/1000) (classification) ...*.* optimization finished, #iter = 484 nu = 0.051825 obj = -22.645856, rho = -2.093138 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 95% (950/1000) (classification) ...*.* optimization finished, #iter = 443 nu = 0.045970 obj = -26.286479, rho = -2.328368 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 95.1% (951/1000) (classification) .....*.* optimization finished, #iter = 665 nu = 0.041988 obj = -30.622874, rho = -2.924132 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 95.7% (957/1000) (classification) .* optimization finished, #iter = 123 nu = 0.169144 obj = -1.055265, rho = -0.223178 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.144891 obj = -1.161941, rho = -0.248817 nSV = 21, nBSV = 10 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *..* optimization finished, #iter = 215 nu = 0.122646 obj = -1.290117, rho = -0.271374 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 142 nu = 0.106964 obj = -1.440613, rho = -0.320327 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 76 nu = 0.092181 obj = -1.622304, rho = -0.280446 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 72 nu = 0.084429 obj = -1.821823, rho = -0.109934 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 59 nu = 0.077506 obj = -2.009144, rho = -0.009487 nSV = 10, nBSV = 4 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 70 nu = 0.067132 obj = -2.170214, rho = 0.006873 nSV = 10, nBSV = 4 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 74 nu = 0.056930 obj = -2.341012, rho = 0.096216 nSV = 10, nBSV = 4 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 98 nu = 0.048910 obj = -2.514223, rho = 0.198650 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 126 nu = 0.045096 obj = -2.634369, rho = -0.071075 nSV = 7, nBSV = 1 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.036693 obj = -2.638938, rho = -0.136416 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.028796 obj = -2.638938, rho = -0.136416 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.022598 obj = -2.638938, rho = -0.136416 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.017734 obj = -2.638938, rho = -0.136416 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.013917 obj = -2.638938, rho = -0.136416 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.010921 obj = -2.638938, rho = -0.136416 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.008571 obj = -2.638938, rho = -0.136416 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.006726 obj = -2.638938, rho = -0.136416 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.005278 obj = -2.638938, rho = -0.136416 nSV = 7, nBSV = 0 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 57 nu = 0.239972 obj = -1.653716, rho = -0.007648 nSV = 27, nBSV = 21 Total nSV = 27 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 67 nu = 0.215522 obj = -1.886159, rho = 0.117522 nSV = 23, nBSV = 18 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *..* optimization finished, #iter = 221 nu = 0.195519 obj = -2.138131, rho = 0.081982 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 74 nu = 0.169699 obj = -2.432978, rho = 0.096753 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.150151 obj = -2.797558, rho = 0.025035 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.138991 obj = -3.211853, rho = -0.073585 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*..* optimization finished, #iter = 305 nu = 0.123661 obj = -3.674624, rho = -0.021361 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 43 nu = 0.111893 obj = -4.190202, rho = -0.000482 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 54 nu = 0.102591 obj = -4.755052, rho = -0.067845 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 84 nu = 0.093051 obj = -5.344066, rho = -0.070551 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.084862 obj = -5.908290, rho = -0.022673 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .* optimization finished, #iter = 191 nu = 0.076077 obj = -6.409785, rho = -0.131096 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) *.* optimization finished, #iter = 144 nu = 0.062861 obj = -6.915853, rho = -0.149866 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .* optimization finished, #iter = 116 nu = 0.055443 obj = -7.472307, rho = -0.278221 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.3% (953/1000) (classification) .* optimization finished, #iter = 160 nu = 0.050666 obj = -7.728819, rho = -0.413062 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.4% (944/1000) (classification) ..*.* optimization finished, #iter = 348 nu = 0.040860 obj = -7.748948, rho = -0.470536 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.4% (944/1000) (classification) ..*.* optimization finished, #iter = 348 nu = 0.032065 obj = -7.748948, rho = -0.470536 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.4% (944/1000) (classification) ..*.* optimization finished, #iter = 348 nu = 0.025163 obj = -7.748948, rho = -0.470536 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.4% (944/1000) (classification) ..*.* optimization finished, #iter = 348 nu = 0.019747 obj = -7.748948, rho = -0.470536 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.4% (944/1000) (classification) ..*.* optimization finished, #iter = 348 nu = 0.015497 obj = -7.748948, rho = -0.470536 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.4% (944/1000) (classification) * optimization finished, #iter = 87 nu = 0.190350 obj = -1.240441, rho = -0.223785 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 189 nu = 0.170517 obj = -1.385977, rho = -0.269531 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 186 nu = 0.151374 obj = -1.527572, rho = -0.321361 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 257 nu = 0.129160 obj = -1.683733, rho = -0.297722 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 183 nu = 0.110636 obj = -1.861720, rho = -0.330247 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 134 nu = 0.095527 obj = -2.074678, rho = -0.326134 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 128 nu = 0.082962 obj = -2.324728, rho = -0.380908 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 92 nu = 0.072586 obj = -2.614552, rho = -0.534305 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 70 nu = 0.064682 obj = -2.942684, rho = -0.419449 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 80 nu = 0.061236 obj = -3.251464, rho = -0.041440 nSV = 9, nBSV = 3 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 156 nu = 0.054566 obj = -3.460026, rho = 0.142116 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ...*...* optimization finished, #iter = 603 nu = 0.046539 obj = -3.612218, rho = 0.167010 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ...*.* optimization finished, #iter = 388 nu = 0.040125 obj = -3.677300, rho = 0.215351 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ...*.* optimization finished, #iter = 389 nu = 0.031488 obj = -3.677300, rho = 0.215533 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ...*.* optimization finished, #iter = 389 nu = 0.024710 obj = -3.677300, rho = 0.215533 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ...*.* optimization finished, #iter = 389 nu = 0.019392 obj = -3.677300, rho = 0.215533 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ...*.* optimization finished, #iter = 389 nu = 0.015218 obj = -3.677300, rho = 0.215533 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ...*.* optimization finished, #iter = 389 nu = 0.011942 obj = -3.677300, rho = 0.215533 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ...*.* optimization finished, #iter = 389 nu = 0.009372 obj = -3.677300, rho = 0.215533 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ...*.* optimization finished, #iter = 389 nu = 0.007355 obj = -3.677300, rho = 0.215533 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.177485 obj = -1.163966, rho = -0.156034 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 62 nu = 0.159211 obj = -1.301058, rho = -0.175122 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) .* optimization finished, #iter = 188 nu = 0.141066 obj = -1.442991, rho = -0.145564 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 99 nu = 0.122418 obj = -1.599672, rho = -0.044587 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.111570 obj = -1.753517, rho = 0.168321 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*..* optimization finished, #iter = 387 nu = 0.095684 obj = -1.882090, rho = 0.245962 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..* optimization finished, #iter = 293 nu = 0.082618 obj = -2.001563, rho = 0.209931 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..* optimization finished, #iter = 248 nu = 0.068378 obj = -2.120526, rho = 0.261803 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 233 nu = 0.056107 obj = -2.258277, rho = 0.302402 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*..* optimization finished, #iter = 335 nu = 0.046662 obj = -2.415215, rho = 0.367450 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 240 nu = 0.039467 obj = -2.594201, rho = 0.450244 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 223 nu = 0.035247 obj = -2.738230, rho = 0.451748 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 183 nu = 0.030241 obj = -2.771645, rho = 0.455898 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 183 nu = 0.023732 obj = -2.771645, rho = 0.455898 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 183 nu = 0.018624 obj = -2.771645, rho = 0.455898 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 183 nu = 0.014615 obj = -2.771645, rho = 0.455898 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 183 nu = 0.011470 obj = -2.771645, rho = 0.455898 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 183 nu = 0.009001 obj = -2.771645, rho = 0.455898 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 183 nu = 0.007063 obj = -2.771645, rho = 0.455898 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 183 nu = 0.005543 obj = -2.771645, rho = 0.455898 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.189158 obj = -1.216219, rho = -0.352499 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.161773 obj = -1.360602, rho = -0.375524 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.143852 obj = -1.522325, rho = -0.392649 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 81 nu = 0.124562 obj = -1.710958, rho = -0.377980 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 90 nu = 0.110739 obj = -1.935072, rho = -0.319576 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 79 nu = 0.102851 obj = -2.157442, rho = -0.154247 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 83 nu = 0.089077 obj = -2.375247, rho = -0.217560 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) *..* optimization finished, #iter = 204 nu = 0.076897 obj = -2.609878, rho = -0.322269 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) *..* optimization finished, #iter = 221 nu = 0.065383 obj = -2.881115, rho = -0.371677 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) ...*..* optimization finished, #iter = 592 nu = 0.055705 obj = -3.206325, rho = -0.401430 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) ..*...* optimization finished, #iter = 572 nu = 0.047526 obj = -3.608375, rho = -0.410326 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..* optimization finished, #iter = 269 nu = 0.041520 obj = -4.110089, rho = -0.440174 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 222 nu = 0.037603 obj = -4.681275, rho = -0.516528 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 211 nu = 0.034799 obj = -5.271374, rho = -0.507258 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.031603 obj = -5.857377, rho = -0.586784 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.029267 obj = -6.345257, rho = -0.705949 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.027205 obj = -6.571668, rho = -0.736422 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.021349 obj = -6.571668, rho = -0.736422 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.016754 obj = -6.571668, rho = -0.736422 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.013148 obj = -6.571668, rho = -0.736422 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 68 nu = 0.211335 obj = -1.443078, rho = -0.135435 nSV = 25, nBSV = 19 Total nSV = 25 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.192568 obj = -1.634303, rho = -0.159330 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.176531 obj = -1.829824, rho = 0.002088 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 156 nu = 0.154122 obj = -2.039825, rho = 0.002701 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 189 nu = 0.132551 obj = -2.273593, rho = 0.007967 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 86 nu = 0.118081 obj = -2.536723, rho = -0.034106 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 111 nu = 0.104166 obj = -2.814237, rho = -0.078365 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *..* optimization finished, #iter = 209 nu = 0.089185 obj = -3.112241, rho = -0.111005 nSV = 16, nBSV = 5 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 136 nu = 0.078253 obj = -3.448774, rho = -0.143887 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 175 nu = 0.069143 obj = -3.818269, rho = -0.210872 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.060403 obj = -4.182436, rho = -0.211121 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 146 nu = 0.052110 obj = -4.583320, rho = -0.289503 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 207 nu = 0.047281 obj = -4.944071, rho = -0.399333 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..*..* optimization finished, #iter = 435 nu = 0.040958 obj = -5.184010, rho = -0.398790 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.035554 obj = -5.291656, rho = -0.391245 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.027901 obj = -5.291656, rho = -0.391245 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.021896 obj = -5.291656, rho = -0.391245 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.017183 obj = -5.291656, rho = -0.391245 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.013485 obj = -5.291656, rho = -0.391245 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*.* optimization finished, #iter = 208 nu = 0.010582 obj = -5.291656, rho = -0.391245 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .*.* optimization finished, #iter = 221 nu = 0.248821 obj = -1.827531, rho = -0.021760 nSV = 29, nBSV = 19 Total nSV = 29 Accuracy = 96% (96/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 75 nu = 0.222026 obj = -2.145107, rho = -0.029566 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 96% (96/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 58 nu = 0.202560 obj = -2.535857, rho = -0.110126 nSV = 25, nBSV = 17 Total nSV = 25 Accuracy = 95% (95/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 70 nu = 0.189941 obj = -2.997499, rho = -0.263663 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 96% (96/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 85 nu = 0.174313 obj = -3.551002, rho = -0.298788 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 97% (97/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.164977 obj = -4.200197, rho = -0.381214 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 98 nu = 0.151709 obj = -4.970826, rho = -0.445026 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) .*.* optimization finished, #iter = 248 nu = 0.141904 obj = -5.850759, rho = -0.483683 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 242 nu = 0.126998 obj = -6.936463, rho = -0.475759 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 159 nu = 0.116874 obj = -8.299012, rho = -0.497806 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 172 nu = 0.108985 obj = -9.986820, rho = -0.583319 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 97% (97/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 161 nu = 0.103912 obj = -12.030894, rho = -0.668075 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 97% (97/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 145 nu = 0.099989 obj = -14.408216, rho = -0.630555 nSV = 14, nBSV = 6 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 204 nu = 0.094958 obj = -17.135179, rho = -0.632853 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 219 nu = 0.088628 obj = -20.311833, rho = -0.693364 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) ...*..* optimization finished, #iter = 530 nu = 0.081995 obj = -24.069060, rho = -0.781589 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*..* optimization finished, #iter = 326 nu = 0.074015 obj = -28.741271, rho = -0.781832 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 184 nu = 0.071956 obj = -34.360420, rho = -1.068891 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) ...*.* optimization finished, #iter = 419 nu = 0.070046 obj = -40.462192, rho = -1.422080 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) ....*.* optimization finished, #iter = 570 nu = 0.064778 obj = -47.215172, rho = -1.596909 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 97 nu = 0.175000 obj = -1.240545, rho = -0.054296 nSV = 21, nBSV = 14 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.157499 obj = -1.432732, rho = 0.040099 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 142 nu = 0.142505 obj = -1.659794, rho = 0.054714 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 140 nu = 0.133180 obj = -1.912882, rho = 0.049131 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 151 nu = 0.124288 obj = -2.171562, rho = 0.057221 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 228 nu = 0.111595 obj = -2.433033, rho = -0.036779 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) ..*..* optimization finished, #iter = 452 nu = 0.097840 obj = -2.726191, rho = -0.026111 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 141 nu = 0.085386 obj = -3.049206, rho = -0.052914 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.074009 obj = -3.433091, rho = -0.067926 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 129 nu = 0.064808 obj = -3.879983, rho = -0.003759 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 218 nu = 0.057977 obj = -4.402482, rho = 0.089896 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 122 nu = 0.055341 obj = -4.929246, rho = 0.378007 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 154 nu = 0.050962 obj = -5.278204, rho = 0.633645 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.042307 obj = -5.582949, rho = 0.371900 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 115 nu = 0.035713 obj = -5.885475, rho = 0.016204 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) .* optimization finished, #iter = 195 nu = 0.031600 obj = -5.993678, rho = 0.020949 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 195 nu = 0.024798 obj = -5.993678, rho = 0.020949 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 195 nu = 0.019461 obj = -5.993678, rho = 0.020949 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 195 nu = 0.015272 obj = -5.993678, rho = 0.020949 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 195 nu = 0.011985 obj = -5.993678, rho = 0.020949 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.191565 obj = -1.259786, rho = -0.190046 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 98.8% (988/1000) (classification) *.* optimization finished, #iter = 143 nu = 0.168619 obj = -1.415009, rho = -0.248694 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 231 nu = 0.149256 obj = -1.583795, rho = -0.255437 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 83 nu = 0.129277 obj = -1.783469, rho = -0.218977 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 64 nu = 0.117236 obj = -2.008203, rho = -0.206591 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 57 nu = 0.105828 obj = -2.220723, rho = -0.331405 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *..* optimization finished, #iter = 215 nu = 0.092560 obj = -2.440299, rho = -0.318776 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 196 nu = 0.079433 obj = -2.665158, rho = -0.287929 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) * optimization finished, #iter = 88 nu = 0.067690 obj = -2.927504, rho = -0.336899 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 75 nu = 0.059073 obj = -3.215572, rho = -0.447110 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.053496 obj = -3.474091, rho = -0.530293 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.047616 obj = -3.646364, rho = -0.616248 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.038575 obj = -3.749307, rho = -0.582836 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.031330 obj = -3.848472, rho = -0.534472 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) .* optimization finished, #iter = 158 nu = 0.025028 obj = -3.944371, rho = -0.519602 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.021248 obj = -4.030119, rho = -0.407277 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.016675 obj = -4.030119, rho = -0.407277 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.013086 obj = -4.030119, rho = -0.407277 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.010269 obj = -4.030119, rho = -0.407277 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .*.* optimization finished, #iter = 227 nu = 0.008059 obj = -4.030119, rho = -0.407277 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 75 nu = 0.179543 obj = -1.117272, rho = -0.054259 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) *.* optimization finished, #iter = 152 nu = 0.155811 obj = -1.225904, rho = -0.063737 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 175 nu = 0.136577 obj = -1.334822, rho = -0.083291 nSV = 19, nBSV = 8 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 183 nu = 0.113840 obj = -1.457613, rho = -0.089184 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 80 nu = 0.099642 obj = -1.597827, rho = -0.082220 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 90 nu = 0.091885 obj = -1.713013, rho = -0.118587 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.079151 obj = -1.757761, rho = -0.200652 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 64 nu = 0.063576 obj = -1.786414, rho = -0.201970 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*....* optimization finished, #iter = 508 nu = 0.050847 obj = -1.806662, rho = -0.191568 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ...*....* optimization finished, #iter = 733 nu = 0.040654 obj = -1.824331, rho = -0.204868 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .....*.......*...* optimization finished, #iter = 1441 nu = 0.032389 obj = -1.827892, rho = -0.215173 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .....*.......*...* optimization finished, #iter = 1441 nu = 0.025418 obj = -1.827892, rho = -0.215173 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .....*.......*...* optimization finished, #iter = 1441 nu = 0.019947 obj = -1.827892, rho = -0.215173 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .....*.......*...* optimization finished, #iter = 1441 nu = 0.015653 obj = -1.827892, rho = -0.215173 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .....*.......*...* optimization finished, #iter = 1441 nu = 0.012284 obj = -1.827892, rho = -0.215173 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .....*.......*...* optimization finished, #iter = 1441 nu = 0.009640 obj = -1.827892, rho = -0.215173 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .....*.......*...* optimization finished, #iter = 1441 nu = 0.007565 obj = -1.827892, rho = -0.215173 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .....*.......*...* optimization finished, #iter = 1441 nu = 0.005937 obj = -1.827892, rho = -0.215173 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .....*.......*...* optimization finished, #iter = 1441 nu = 0.004659 obj = -1.827892, rho = -0.215173 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .....*.......*...* optimization finished, #iter = 1441 nu = 0.003656 obj = -1.827892, rho = -0.215173 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 34 nu = 0.214059 obj = -1.506390, rho = -0.176580 nSV = 24, nBSV = 19 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 61 nu = 0.194402 obj = -1.729050, rho = -0.263477 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 46 nu = 0.177328 obj = -1.981323, rho = -0.249979 nSV = 21, nBSV = 15 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.159647 obj = -2.251251, rho = -0.231265 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 93 nu = 0.138063 obj = -2.579036, rho = -0.218883 nSV = 21, nBSV = 11 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 53 nu = 0.129729 obj = -2.961611, rho = -0.077134 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.117347 obj = -3.355804, rho = -0.119449 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *..* optimization finished, #iter = 261 nu = 0.107195 obj = -3.771667, rho = -0.174711 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 148 nu = 0.095473 obj = -4.186635, rho = -0.056312 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 91 nu = 0.084442 obj = -4.622208, rho = 0.013162 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 222 nu = 0.071515 obj = -5.077406, rho = 0.026088 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.066390 obj = -5.533260, rho = 0.178070 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.061026 obj = -5.778507, rho = 0.308019 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 140 nu = 0.049555 obj = -5.788208, rho = 0.336793 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 140 nu = 0.038889 obj = -5.788208, rho = 0.336793 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 140 nu = 0.030519 obj = -5.788208, rho = 0.336793 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 140 nu = 0.023950 obj = -5.788208, rho = 0.336793 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 140 nu = 0.018795 obj = -5.788208, rho = 0.336793 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 140 nu = 0.014749 obj = -5.788208, rho = 0.336793 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .* optimization finished, #iter = 140 nu = 0.011575 obj = -5.788208, rho = 0.336793 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 54 nu = 0.198528 obj = -1.313403, rho = -0.304106 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 200 nu = 0.176382 obj = -1.472500, rho = -0.149796 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 96 nu = 0.152058 obj = -1.659347, rho = -0.174725 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 60 nu = 0.138130 obj = -1.870341, rho = -0.292586 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 38 nu = 0.120834 obj = -2.102585, rho = -0.297639 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.108951 obj = -2.354482, rho = -0.297236 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) ..*.* optimization finished, #iter = 255 nu = 0.095594 obj = -2.621880, rho = -0.301897 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 155 nu = 0.083144 obj = -2.912215, rho = -0.322518 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 108 nu = 0.072534 obj = -3.247972, rho = -0.399969 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.064037 obj = -3.612778, rho = -0.494654 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 156 nu = 0.058904 obj = -3.951797, rho = -0.433062 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) ..* optimization finished, #iter = 222 nu = 0.050352 obj = -4.253988, rho = -0.602680 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 248 nu = 0.043224 obj = -4.559176, rho = -0.842115 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .* optimization finished, #iter = 159 nu = 0.038473 obj = -4.799308, rho = -1.368819 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 175 nu = 0.032439 obj = -4.827054, rho = -1.620569 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .* optimization finished, #iter = 175 nu = 0.025457 obj = -4.827054, rho = -1.620569 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .* optimization finished, #iter = 175 nu = 0.019978 obj = -4.827054, rho = -1.620569 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .* optimization finished, #iter = 175 nu = 0.015678 obj = -4.827054, rho = -1.620569 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .* optimization finished, #iter = 175 nu = 0.012303 obj = -4.827054, rho = -1.620569 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .* optimization finished, #iter = 175 nu = 0.009655 obj = -4.827054, rho = -1.620569 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.195947 obj = -1.245511, rho = 0.062535 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 84 nu = 0.166659 obj = -1.387979, rho = 0.061949 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 96 nu = 0.148505 obj = -1.556896, rho = 0.035881 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) * optimization finished, #iter = 69 nu = 0.128658 obj = -1.743620, rho = 0.128348 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*....* optimization finished, #iter = 509 nu = 0.112899 obj = -1.960694, rho = 0.182427 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 92 nu = 0.098383 obj = -2.213820, rho = 0.231961 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 76 nu = 0.086656 obj = -2.505602, rho = 0.275839 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 167 nu = 0.077075 obj = -2.844332, rho = 0.267150 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 99 nu = 0.067662 obj = -3.238156, rho = 0.281869 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 73 nu = 0.061318 obj = -3.688103, rho = 0.268335 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 151 nu = 0.057109 obj = -4.155434, rho = 0.242543 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.054312 obj = -4.554830, rho = 0.173740 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..*...* optimization finished, #iter = 582 nu = 0.049091 obj = -4.749047, rho = 0.090932 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ...*.* optimization finished, #iter = 431 nu = 0.041250 obj = -4.818230, rho = 0.094355 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ...*.* optimization finished, #iter = 431 nu = 0.032371 obj = -4.818230, rho = 0.094355 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ...*.* optimization finished, #iter = 431 nu = 0.025404 obj = -4.818230, rho = 0.094355 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ...*.* optimization finished, #iter = 431 nu = 0.019936 obj = -4.818230, rho = 0.094355 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ...*.* optimization finished, #iter = 431 nu = 0.015645 obj = -4.818230, rho = 0.094355 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ...*.* optimization finished, #iter = 431 nu = 0.012277 obj = -4.818230, rho = 0.094355 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ...*.* optimization finished, #iter = 431 nu = 0.009635 obj = -4.818230, rho = 0.094355 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 54 nu = 0.181333 obj = -1.164018, rho = -0.122189 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 69 nu = 0.158511 obj = -1.294119, rho = -0.111347 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 131 nu = 0.144408 obj = -1.428254, rho = -0.037840 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 70 nu = 0.123805 obj = -1.553569, rho = 0.013141 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 78 nu = 0.106351 obj = -1.692532, rho = 0.032407 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.092557 obj = -1.828528, rho = 0.030526 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 97 nu = 0.077239 obj = -1.972840, rho = 0.067012 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 99 nu = 0.065552 obj = -2.134978, rho = 0.132302 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) * optimization finished, #iter = 69 nu = 0.057955 obj = -2.289950, rho = 0.177778 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 90 nu = 0.048133 obj = -2.429600, rho = 0.120315 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 91 nu = 0.040864 obj = -2.571834, rho = 0.102198 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 134 nu = 0.035347 obj = -2.665784, rho = 0.119132 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.029351 obj = -2.690535, rho = 0.066787 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.023033 obj = -2.690535, rho = 0.066787 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.018076 obj = -2.690535, rho = 0.066787 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.014185 obj = -2.690535, rho = 0.066787 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.011132 obj = -2.690535, rho = 0.066787 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.008736 obj = -2.690535, rho = 0.066787 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.006856 obj = -2.690535, rho = 0.066787 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.005380 obj = -2.690535, rho = 0.066787 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.191744 obj = -1.194464, rho = -0.234021 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 82 nu = 0.165953 obj = -1.311152, rho = -0.233911 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 134 nu = 0.140145 obj = -1.445962, rho = -0.209578 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) .*.* optimization finished, #iter = 204 nu = 0.120225 obj = -1.609166, rho = -0.187021 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 123 nu = 0.105736 obj = -1.795107, rho = -0.285182 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 67 nu = 0.091825 obj = -1.999617, rho = -0.254590 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 67 nu = 0.080849 obj = -2.234641, rho = -0.222205 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .* optimization finished, #iter = 177 nu = 0.072222 obj = -2.483027, rho = -0.260974 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 166 nu = 0.063992 obj = -2.710231, rho = -0.316151 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 183 nu = 0.056762 obj = -2.931437, rho = -0.356505 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 94 nu = 0.046817 obj = -3.153937, rho = -0.337148 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 163 nu = 0.040067 obj = -3.418429, rho = -0.180873 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 183 nu = 0.035020 obj = -3.663823, rho = -0.009610 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 59 nu = 0.031767 obj = -3.809889, rho = 0.205711 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 175 nu = 0.025631 obj = -3.814418, rho = 0.250483 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 175 nu = 0.020114 obj = -3.814418, rho = 0.250483 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 175 nu = 0.015785 obj = -3.814418, rho = 0.250483 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 175 nu = 0.012387 obj = -3.814418, rho = 0.250483 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 175 nu = 0.009721 obj = -3.814418, rho = 0.250483 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 175 nu = 0.007629 obj = -3.814418, rho = 0.250483 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .*.* optimization finished, #iter = 270 nu = 0.180870 obj = -1.220894, rho = -0.256584 nSV = 23, nBSV = 14 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 92 nu = 0.161020 obj = -1.387596, rho = -0.333853 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 86 nu = 0.145443 obj = -1.567371, rho = -0.345406 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 69 nu = 0.128515 obj = -1.762094, rho = -0.311872 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 72 nu = 0.114670 obj = -1.983160, rho = -0.274061 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 83 nu = 0.105675 obj = -2.210073, rho = -0.234022 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 175 nu = 0.093091 obj = -2.414173, rho = -0.183048 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.077527 obj = -2.640607, rho = -0.183172 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.065443 obj = -2.925267, rho = -0.181399 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 124 nu = 0.057943 obj = -3.263291, rho = -0.309267 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 166 nu = 0.050439 obj = -3.607615, rho = -0.403114 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .* optimization finished, #iter = 155 nu = 0.044359 obj = -3.987606, rho = -0.329686 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 185 nu = 0.039673 obj = -4.364302, rho = -0.237339 nSV = 9, nBSV = 2 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) ..*..* optimization finished, #iter = 435 nu = 0.036283 obj = -4.641755, rho = -0.168391 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 209 nu = 0.030292 obj = -4.810263, rho = -0.205169 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 266 nu = 0.025578 obj = -4.850292, rho = -0.237798 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 266 nu = 0.020072 obj = -4.850292, rho = -0.237798 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 266 nu = 0.015752 obj = -4.850292, rho = -0.237798 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 266 nu = 0.012361 obj = -4.850292, rho = -0.237798 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .*.* optimization finished, #iter = 266 nu = 0.009701 obj = -4.850292, rho = -0.237798 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 47 nu = 0.194518 obj = -1.331238, rho = -0.074181 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) * optimization finished, #iter = 39 nu = 0.174101 obj = -1.517607, rho = -0.099424 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.155077 obj = -1.728572, rho = -0.170722 nSV = 19, nBSV = 12 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 197 nu = 0.135669 obj = -1.981213, rho = -0.187747 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*..* optimization finished, #iter = 394 nu = 0.120227 obj = -2.290999, rho = -0.213535 nSV = 17, nBSV = 8 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 196 nu = 0.109740 obj = -2.664941, rho = -0.233168 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) .**.* optimization finished, #iter = 137 nu = 0.101086 obj = -3.081837, rho = -0.250080 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.092345 obj = -3.547227, rho = -0.207336 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) *...* optimization finished, #iter = 378 nu = 0.085999 obj = -4.049214, rho = -0.154261 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 96.8% (968/1000) (classification) ...*.* optimization finished, #iter = 470 nu = 0.076126 obj = -4.587803, rho = -0.122246 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) .......*.* optimization finished, #iter = 873 nu = 0.066860 obj = -5.249967, rho = -0.085018 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.6% (966/1000) (classification) ...*..............* optimization finished, #iter = 1754 nu = 0.060188 obj = -6.016124, rho = -0.018116 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) ....*.* optimization finished, #iter = 530 nu = 0.055909 obj = -6.857182, rho = 0.162220 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.5% (965/1000) (classification) ...*.* optimization finished, #iter = 429 nu = 0.050163 obj = -7.697934, rho = 0.244271 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.1% (961/1000) (classification) ...*.....* optimization finished, #iter = 858 nu = 0.043646 obj = -8.661216, rho = 0.191441 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 95.9% (959/1000) (classification) ....*.* optimization finished, #iter = 514 nu = 0.037626 obj = -9.845993, rho = 0.180427 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 95.8% (958/1000) (classification) ........*....* optimization finished, #iter = 1270 nu = 0.033104 obj = -11.278507, rho = 0.153715 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 95.2% (952/1000) (classification) ...*.* optimization finished, #iter = 467 nu = 0.029031 obj = -13.074094, rho = 0.171243 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 95.2% (952/1000) (classification) ..*..* optimization finished, #iter = 409 nu = 0.027163 obj = -15.182901, rho = 0.172181 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 95% (950/1000) (classification) ..*.* optimization finished, #iter = 394 nu = 0.025683 obj = -17.407448, rho = 0.193528 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 94.6% (946/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.168771 obj = -1.042565, rho = -0.149735 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.144982 obj = -1.147394, rho = -0.173602 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 99% (990/1000) (classification) .* optimization finished, #iter = 158 nu = 0.125094 obj = -1.260701, rho = -0.213181 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 90 nu = 0.105587 obj = -1.391644, rho = -0.187042 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 72 nu = 0.093230 obj = -1.544136, rho = -0.232224 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 99.1% (991/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.088807 obj = -1.670021, rho = -0.427942 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.7% (987/1000) (classification) .*.* optimization finished, #iter = 283 nu = 0.073874 obj = -1.749056, rho = -0.460202 nSV = 14, nBSV = 3 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 191 nu = 0.060170 obj = -1.834864, rho = -0.458846 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) ...*..* optimization finished, #iter = 558 nu = 0.049874 obj = -1.927265, rho = -0.475880 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) ...*.* optimization finished, #iter = 416 nu = 0.041343 obj = -2.019492, rho = -0.481230 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.4% (984/1000) (classification) ..* optimization finished, #iter = 296 nu = 0.034153 obj = -2.113257, rho = -0.507839 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..*...* optimization finished, #iter = 591 nu = 0.029030 obj = -2.175981, rho = -0.578071 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) ..*.* optimization finished, #iter = 301 nu = 0.023894 obj = -2.189456, rho = -0.614876 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..*.* optimization finished, #iter = 301 nu = 0.018751 obj = -2.189456, rho = -0.614876 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..*.* optimization finished, #iter = 301 nu = 0.014715 obj = -2.189456, rho = -0.614876 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..*.* optimization finished, #iter = 301 nu = 0.011548 obj = -2.189456, rho = -0.614876 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..*.* optimization finished, #iter = 301 nu = 0.009062 obj = -2.189456, rho = -0.614876 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..*.* optimization finished, #iter = 301 nu = 0.007112 obj = -2.189456, rho = -0.614876 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..*.* optimization finished, #iter = 301 nu = 0.005581 obj = -2.189456, rho = -0.614876 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..*.* optimization finished, #iter = 301 nu = 0.004380 obj = -2.189456, rho = -0.614876 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) *.* optimization finished, #iter = 106 nu = 0.205697 obj = -1.419968, rho = -0.468219 nSV = 24, nBSV = 15 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.184951 obj = -1.622248, rho = -0.509850 nSV = 24, nBSV = 14 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 49 nu = 0.164313 obj = -1.856831, rho = -0.557124 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 98% (98/100) (classification) Accuracy = 97.4% (974/1000) (classification) * optimization finished, #iter = 48 nu = 0.150879 obj = -2.121222, rho = -0.606956 nSV = 19, nBSV = 11 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*..........* optimization finished, #iter = 1107 nu = 0.133572 obj = -2.407422, rho = -0.645710 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) *...* optimization finished, #iter = 396 nu = 0.116080 obj = -2.757714, rho = -0.642010 nSV = 19, nBSV = 8 Total nSV = 19 Accuracy = 97% (97/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 204 nu = 0.102380 obj = -3.192553, rho = -0.635758 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 97% (97/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 171 nu = 0.092995 obj = -3.724713, rho = -0.591677 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 117 nu = 0.086765 obj = -4.334329, rho = -0.473123 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 68 nu = 0.079444 obj = -5.011349, rho = -0.387512 nSV = 12, nBSV = 5 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 78 nu = 0.074135 obj = -5.761406, rho = -0.212042 nSV = 11, nBSV = 4 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 96 nu = 0.068087 obj = -6.548497, rho = -0.167095 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) .* optimization finished, #iter = 145 nu = 0.062261 obj = -7.334567, rho = -0.124928 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 88 nu = 0.056089 obj = -8.113139, rho = -0.264325 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 104 nu = 0.050897 obj = -8.807343, rho = -0.465450 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 225 nu = 0.046546 obj = -9.222124, rho = -0.696288 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..* optimization finished, #iter = 285 nu = 0.038291 obj = -9.254186, rho = -0.761944 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..* optimization finished, #iter = 285 nu = 0.030049 obj = -9.254186, rho = -0.761944 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..* optimization finished, #iter = 285 nu = 0.023581 obj = -9.254186, rho = -0.761944 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..* optimization finished, #iter = 285 nu = 0.018506 obj = -9.254186, rho = -0.761944 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) * optimization finished, #iter = 59 nu = 0.163193 obj = -1.022124, rho = -0.276778 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 42 nu = 0.142121 obj = -1.124763, rho = -0.260221 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 43 nu = 0.126483 obj = -1.223029, rho = -0.138206 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 56 nu = 0.110472 obj = -1.314253, rho = -0.046145 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 118 nu = 0.095509 obj = -1.382745, rho = -0.034047 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.078257 obj = -1.445877, rho = -0.068025 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 153 nu = 0.066699 obj = -1.496726, rho = 0.041953 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..*..* optimization finished, #iter = 464 nu = 0.055299 obj = -1.508690, rho = 0.064360 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..*..* optimization finished, #iter = 464 nu = 0.043397 obj = -1.508690, rho = 0.064360 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..*..* optimization finished, #iter = 464 nu = 0.034056 obj = -1.508690, rho = 0.064360 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..*..* optimization finished, #iter = 464 nu = 0.026726 obj = -1.508690, rho = 0.064360 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..*..* optimization finished, #iter = 464 nu = 0.020973 obj = -1.508690, rho = 0.064360 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..*..* optimization finished, #iter = 464 nu = 0.016459 obj = -1.508690, rho = 0.064360 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..*..* optimization finished, #iter = 464 nu = 0.012916 obj = -1.508690, rho = 0.064360 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..*..* optimization finished, #iter = 464 nu = 0.010136 obj = -1.508690, rho = 0.064360 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..*..* optimization finished, #iter = 464 nu = 0.007955 obj = -1.508690, rho = 0.064360 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..*..* optimization finished, #iter = 464 nu = 0.006242 obj = -1.508690, rho = 0.064360 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..*..* optimization finished, #iter = 464 nu = 0.004899 obj = -1.508690, rho = 0.064360 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..*..* optimization finished, #iter = 464 nu = 0.003844 obj = -1.508690, rho = 0.064360 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) ..*..* optimization finished, #iter = 464 nu = 0.003017 obj = -1.508690, rho = 0.064360 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.193718 obj = -1.308829, rho = -0.241637 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 141 nu = 0.174616 obj = -1.478186, rho = -0.259422 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 61 nu = 0.154469 obj = -1.666676, rho = -0.238547 nSV = 18, nBSV = 13 Total nSV = 18 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 65 nu = 0.139523 obj = -1.876082, rho = -0.259025 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.125915 obj = -2.080543, rho = -0.288967 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97% (970/1000) (classification) .*..* optimization finished, #iter = 372 nu = 0.107186 obj = -2.301706, rho = -0.335579 nSV = 16, nBSV = 4 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*.......* optimization finished, #iter = 840 nu = 0.090779 obj = -2.576136, rho = -0.336226 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 96.8% (968/1000) (classification) ..* optimization finished, #iter = 286 nu = 0.080926 obj = -2.905651, rho = -0.264445 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) .*..........*.* optimization finished, #iter = 1182 nu = 0.073286 obj = -3.247908, rho = -0.240098 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 96.1% (961/1000) (classification) *..* optimization finished, #iter = 289 nu = 0.063498 obj = -3.592719, rho = -0.236561 nSV = 13, nBSV = 2 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 95.7% (957/1000) (classification) .* optimization finished, #iter = 155 nu = 0.056049 obj = -3.999309, rho = -0.202906 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .*.* optimization finished, #iter = 201 nu = 0.051367 obj = -4.376430, rho = -0.070384 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.2% (952/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.046705 obj = -4.617500, rho = -0.073202 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95% (950/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.039994 obj = -4.670907, rho = -0.040520 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.5% (945/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.031386 obj = -4.670907, rho = -0.040520 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.5% (945/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.024630 obj = -4.670907, rho = -0.040520 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.5% (945/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.019329 obj = -4.670907, rho = -0.040520 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.5% (945/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.015169 obj = -4.670907, rho = -0.040520 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.5% (945/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.011904 obj = -4.670907, rho = -0.040520 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.5% (945/1000) (classification) *.* optimization finished, #iter = 132 nu = 0.009342 obj = -4.670907, rho = -0.040520 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 94.5% (945/1000) (classification) *.* optimization finished, #iter = 105 nu = 0.172347 obj = -1.164015, rho = -0.235390 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.150668 obj = -1.326447, rho = -0.232923 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 48 nu = 0.140000 obj = -1.511813, rho = -0.331063 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 55 nu = 0.135766 obj = -1.667197, rho = -0.581055 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.116284 obj = -1.790597, rho = -0.596459 nSV = 16, nBSV = 7 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 114 nu = 0.097037 obj = -1.926087, rho = -0.590888 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) *.* optimization finished, #iter = 120 nu = 0.082800 obj = -2.070807, rho = -0.557306 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) .* optimization finished, #iter = 166 nu = 0.069312 obj = -2.225796, rho = -0.567545 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) .*..*.* optimization finished, #iter = 437 nu = 0.057902 obj = -2.404895, rho = -0.546085 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.1% (961/1000) (classification) *.* optimization finished, #iter = 119 nu = 0.050405 obj = -2.598147, rho = -0.460877 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) .* optimization finished, #iter = 166 nu = 0.045598 obj = -2.732790, rho = -0.277834 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .*.* optimization finished, #iter = 273 nu = 0.038291 obj = -2.754217, rho = -0.161976 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..* optimization finished, #iter = 270 nu = 0.030048 obj = -2.754216, rho = -0.162244 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..* optimization finished, #iter = 270 nu = 0.023580 obj = -2.754216, rho = -0.162244 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..* optimization finished, #iter = 270 nu = 0.018505 obj = -2.754216, rho = -0.162244 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..* optimization finished, #iter = 270 nu = 0.014522 obj = -2.754216, rho = -0.162244 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..* optimization finished, #iter = 270 nu = 0.011396 obj = -2.754216, rho = -0.162244 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..* optimization finished, #iter = 270 nu = 0.008943 obj = -2.754216, rho = -0.162244 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..* optimization finished, #iter = 270 nu = 0.007018 obj = -2.754216, rho = -0.162244 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) ..* optimization finished, #iter = 270 nu = 0.005508 obj = -2.754216, rho = -0.162244 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.2% (962/1000) (classification) * optimization finished, #iter = 64 nu = 0.186164 obj = -1.235235, rho = -0.164942 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 97 nu = 0.164640 obj = -1.396126, rho = -0.177614 nSV = 19, nBSV = 14 Total nSV = 19 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 86 nu = 0.147310 obj = -1.573236, rho = -0.144273 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 88 nu = 0.131004 obj = -1.758496, rho = -0.146435 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) * optimization finished, #iter = 70 nu = 0.115198 obj = -1.970607, rho = -0.091440 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.104352 obj = -2.186923, rho = -0.061872 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) *.* optimization finished, #iter = 133 nu = 0.092380 obj = -2.388087, rho = -0.124997 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.080440 obj = -2.570505, rho = -0.138678 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.066814 obj = -2.773536, rho = -0.107102 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification) * optimization finished, #iter = 53 nu = 0.058393 obj = -2.972382, rho = -0.102960 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 65 nu = 0.048493 obj = -3.171648, rho = -0.016263 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 107 nu = 0.040733 obj = -3.389936, rho = 0.093373 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) * optimization finished, #iter = 59 nu = 0.034263 obj = -3.625372, rho = 0.166176 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 130 nu = 0.030912 obj = -3.802109, rho = 0.207204 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.025673 obj = -3.821016, rho = 0.201179 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.020147 obj = -3.821016, rho = 0.201179 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.015811 obj = -3.821016, rho = 0.201179 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.012408 obj = -3.821016, rho = 0.201179 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.009737 obj = -3.821016, rho = 0.201179 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 213 nu = 0.007641 obj = -3.821016, rho = 0.201179 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 68 nu = 0.221049 obj = -1.496830, rho = 0.085206 nSV = 26, nBSV = 19 Total nSV = 26 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 79 nu = 0.198496 obj = -1.695795, rho = 0.103127 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 71 nu = 0.178302 obj = -1.915263, rho = 0.090416 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98.1% (981/1000) (classification) *..* optimization finished, #iter = 210 nu = 0.157518 obj = -2.149914, rho = 0.076664 nSV = 22, nBSV = 13 Total nSV = 22 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 116 nu = 0.137088 obj = -2.426385, rho = 0.091953 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 128 nu = 0.129922 obj = -2.712785, rho = 0.304294 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*..* optimization finished, #iter = 474 nu = 0.112155 obj = -2.964682, rho = 0.381141 nSV = 16, nBSV = 6 Total nSV = 16 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) ....*.* optimization finished, #iter = 578 nu = 0.094874 obj = -3.265233, rho = 0.347233 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..*.* optimization finished, #iter = 355 nu = 0.079984 obj = -3.635203, rho = 0.339266 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 97.5% (975/1000) (classification) ..* optimization finished, #iter = 295 nu = 0.068620 obj = -4.098351, rho = 0.326335 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 98% (98/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 132 nu = 0.061812 obj = -4.650439, rho = 0.274903 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 99% (99/100) (classification) Accuracy = 97.5% (975/1000) (classification) ...*.....* optimization finished, #iter = 873 nu = 0.054898 obj = -5.240018, rho = 0.149123 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) ..* optimization finished, #iter = 287 nu = 0.050811 obj = -5.869508, rho = -0.017486 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*..* optimization finished, #iter = 308 nu = 0.047973 obj = -6.368739, rho = -0.188552 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .* optimization finished, #iter = 187 nu = 0.039661 obj = -6.746597, rho = -0.170824 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 192 nu = 0.033150 obj = -7.180748, rho = -0.119927 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.7% (967/1000) (classification) ....* optimization finished, #iter = 490 nu = 0.030628 obj = -7.399608, rho = 0.138553 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) ....*.* optimization finished, #iter = 515 nu = 0.024035 obj = -7.399608, rho = 0.138403 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) ....*.* optimization finished, #iter = 515 nu = 0.018862 obj = -7.399608, rho = 0.138403 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) ....*.* optimization finished, #iter = 515 nu = 0.014802 obj = -7.399608, rho = 0.138403 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) * optimization finished, #iter = 66 nu = 0.217275 obj = -1.527055, rho = -0.064454 nSV = 27, nBSV = 18 Total nSV = 27 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 59 nu = 0.197027 obj = -1.762007, rho = 0.014756 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 93 nu = 0.180368 obj = -2.019982, rho = 0.090056 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 127 nu = 0.165069 obj = -2.295153, rho = 0.131826 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 280 nu = 0.145685 obj = -2.593068, rho = 0.158629 nSV = 20, nBSV = 10 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 199 nu = 0.125808 obj = -2.960074, rho = 0.159684 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.114772 obj = -3.396989, rho = 0.129654 nSV = 17, nBSV = 9 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 172 nu = 0.105795 obj = -3.857523, rho = 0.109239 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) ..* optimization finished, #iter = 286 nu = 0.097943 obj = -4.326617, rho = 0.124717 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 135 nu = 0.085014 obj = -4.793933, rho = 0.137115 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 250 nu = 0.072038 obj = -5.345324, rho = 0.129750 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) *.* optimization finished, #iter = 172 nu = 0.061426 obj = -6.046423, rho = 0.123239 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .* optimization finished, #iter = 192 nu = 0.056898 obj = -6.842341, rho = 0.150213 nSV = 14, nBSV = 4 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 214 nu = 0.052149 obj = -7.622217, rho = 0.136798 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) .* optimization finished, #iter = 170 nu = 0.045635 obj = -8.376680, rho = 0.136768 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 134 nu = 0.038621 obj = -9.241780, rho = 0.187634 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.037438 obj = -9.985216, rho = 0.501664 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 200 nu = 0.032804 obj = -10.104120, rho = 0.689332 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 200 nu = 0.025744 obj = -10.104120, rho = 0.689332 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) .*.* optimization finished, #iter = 200 nu = 0.020203 obj = -10.104120, rho = 0.689332 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 67 nu = 0.214723 obj = -1.376355, rho = -0.083442 nSV = 24, nBSV = 16 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) * optimization finished, #iter = 73 nu = 0.183500 obj = -1.538257, rho = -0.082941 nSV = 23, nBSV = 15 Total nSV = 23 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 79 nu = 0.161569 obj = -1.732088, rho = -0.160324 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) * optimization finished, #iter = 62 nu = 0.145310 obj = -1.941595, rho = -0.218431 nSV = 18, nBSV = 12 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) *.* optimization finished, #iter = 159 nu = 0.128606 obj = -2.162711, rho = -0.237127 nSV = 18, nBSV = 10 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 98.6% (986/1000) (classification) .* optimization finished, #iter = 165 nu = 0.113118 obj = -2.395857, rho = -0.237833 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.7% (987/1000) (classification) *.* optimization finished, #iter = 135 nu = 0.102522 obj = -2.624788, rho = -0.009694 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..*..* optimization finished, #iter = 427 nu = 0.089433 obj = -2.822478, rho = 0.193038 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.079237 obj = -2.962732, rho = 0.427602 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 224 nu = 0.064925 obj = -3.048590, rho = 0.468238 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 167 nu = 0.054890 obj = -3.098112, rho = 0.389410 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 178 nu = 0.043073 obj = -3.098112, rho = 0.389411 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 178 nu = 0.033802 obj = -3.098112, rho = 0.389411 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 178 nu = 0.026527 obj = -3.098112, rho = 0.389411 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 178 nu = 0.020817 obj = -3.098112, rho = 0.389411 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 178 nu = 0.016336 obj = -3.098112, rho = 0.389411 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 178 nu = 0.012820 obj = -3.098112, rho = 0.389411 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 178 nu = 0.010061 obj = -3.098112, rho = 0.389411 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 178 nu = 0.007895 obj = -3.098112, rho = 0.389411 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 178 nu = 0.006196 obj = -3.098112, rho = 0.389411 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 51 nu = 0.224735 obj = -1.472860, rho = -0.245838 nSV = 25, nBSV = 20 Total nSV = 25 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 69 nu = 0.205551 obj = -1.631826, rho = -0.348445 nSV = 23, nBSV = 16 Total nSV = 23 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 71 nu = 0.176217 obj = -1.799413, rho = -0.357247 nSV = 20, nBSV = 13 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 151 nu = 0.153673 obj = -1.988831, rho = -0.289824 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 57 nu = 0.130925 obj = -2.203303, rho = -0.324840 nSV = 16, nBSV = 10 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 60 nu = 0.116223 obj = -2.441395, rho = -0.351718 nSV = 14, nBSV = 9 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 95 nu = 0.103841 obj = -2.662587, rho = -0.440686 nSV = 13, nBSV = 5 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 139 nu = 0.088626 obj = -2.878343, rho = -0.483632 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .* optimization finished, #iter = 184 nu = 0.074302 obj = -3.120923, rho = -0.468669 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) .*.* optimization finished, #iter = 256 nu = 0.064627 obj = -3.359314, rho = -0.422856 nSV = 12, nBSV = 1 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.054480 obj = -3.619449, rho = -0.336280 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.047701 obj = -3.861395, rho = -0.251017 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) ..* optimization finished, #iter = 279 nu = 0.041459 obj = -4.019247, rho = -0.137625 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .* optimization finished, #iter = 186 nu = 0.034605 obj = -4.041866, rho = -0.074900 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 186 nu = 0.027156 obj = -4.041866, rho = -0.074900 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 186 nu = 0.021311 obj = -4.041866, rho = -0.074900 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 186 nu = 0.016724 obj = -4.041866, rho = -0.074900 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 186 nu = 0.013124 obj = -4.041866, rho = -0.074900 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 186 nu = 0.010300 obj = -4.041866, rho = -0.074900 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 186 nu = 0.008083 obj = -4.041866, rho = -0.074900 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.177487 obj = -1.127518, rho = 0.190511 nSV = 22, nBSV = 15 Total nSV = 22 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 121 nu = 0.155062 obj = -1.247631, rho = 0.266388 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 57 nu = 0.133184 obj = -1.385490, rho = 0.246008 nSV = 18, nBSV = 11 Total nSV = 18 Accuracy = 100% (100/100) (classification) Accuracy = 97% (970/1000) (classification) * optimization finished, #iter = 70 nu = 0.120006 obj = -1.531963, rho = 0.284682 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) * optimization finished, #iter = 92 nu = 0.105986 obj = -1.667658, rho = 0.336667 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .*.*.* optimization finished, #iter = 309 nu = 0.088492 obj = -1.808961, rho = 0.355785 nSV = 15, nBSV = 5 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 147 nu = 0.078585 obj = -1.949050, rho = 0.443339 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 149 nu = 0.067763 obj = -2.068533, rho = 0.452196 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 122 nu = 0.059258 obj = -2.148435, rho = 0.452135 nSV = 10, nBSV = 2 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) .* optimization finished, #iter = 183 nu = 0.048366 obj = -2.164340, rho = 0.457685 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ..*.* optimization finished, #iter = 320 nu = 0.038114 obj = -2.176120, rho = 0.462815 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.3% (963/1000) (classification) ....* optimization finished, #iter = 490 nu = 0.030258 obj = -2.188130, rho = 0.457521 nSV = 9, nBSV = 1 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .....*.* optimization finished, #iter = 606 nu = 0.023874 obj = -2.188484, rho = 0.448143 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .....*.* optimization finished, #iter = 606 nu = 0.018735 obj = -2.188484, rho = 0.448143 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .....*.* optimization finished, #iter = 606 nu = 0.014703 obj = -2.188484, rho = 0.448143 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .....*.* optimization finished, #iter = 606 nu = 0.011538 obj = -2.188484, rho = 0.448143 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .....*.* optimization finished, #iter = 606 nu = 0.009055 obj = -2.188484, rho = 0.448143 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .....*.* optimization finished, #iter = 606 nu = 0.007106 obj = -2.188484, rho = 0.448143 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .....*.* optimization finished, #iter = 606 nu = 0.005576 obj = -2.188484, rho = 0.448143 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .....*.* optimization finished, #iter = 606 nu = 0.004376 obj = -2.188484, rho = 0.448143 nSV = 9, nBSV = 0 Total nSV = 9 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) *.* optimization finished, #iter = 101 nu = 0.171806 obj = -1.084049, rho = -0.038761 nSV = 21, nBSV = 13 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) *.* optimization finished, #iter = 169 nu = 0.144930 obj = -1.206024, rho = -0.048577 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 52 nu = 0.128250 obj = -1.354659, rho = -0.014396 nSV = 14, nBSV = 9 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 45 nu = 0.110643 obj = -1.521207, rho = 0.005582 nSV = 14, nBSV = 8 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 70 nu = 0.100661 obj = -1.704734, rho = -0.077078 nSV = 13, nBSV = 7 Total nSV = 13 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) * optimization finished, #iter = 94 nu = 0.091907 obj = -1.883075, rho = -0.146949 nSV = 12, nBSV = 6 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 98% (980/1000) (classification) .* optimization finished, #iter = 191 nu = 0.078507 obj = -2.053514, rho = -0.021414 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) .* optimization finished, #iter = 135 nu = 0.066108 obj = -2.250418, rho = -0.005365 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.056290 obj = -2.489300, rho = -0.053658 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.5% (975/1000) (classification) *.* optimization finished, #iter = 122 nu = 0.050428 obj = -2.736884, rho = 0.066055 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) * optimization finished, #iter = 98 nu = 0.044031 obj = -2.975530, rho = -0.021695 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 115 nu = 0.037078 obj = -3.231308, rho = -0.087629 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 122 nu = 0.032330 obj = -3.514643, rho = -0.205641 nSV = 7, nBSV = 1 Total nSV = 7 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 152 nu = 0.029756 obj = -3.716085, rho = -0.426092 nSV = 8, nBSV = 1 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.6% (966/1000) (classification) .* optimization finished, #iter = 169 nu = 0.025154 obj = -3.743538, rho = -0.543041 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 169 nu = 0.019740 obj = -3.743538, rho = -0.543041 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 169 nu = 0.015491 obj = -3.743538, rho = -0.543041 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 169 nu = 0.012157 obj = -3.743538, rho = -0.543041 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 169 nu = 0.009540 obj = -3.743538, rho = -0.543041 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) .* optimization finished, #iter = 169 nu = 0.007487 obj = -3.743538, rho = -0.543041 nSV = 8, nBSV = 0 Total nSV = 8 Accuracy = 100% (100/100) (classification) Accuracy = 96.5% (965/1000) (classification) *.* optimization finished, #iter = 146 nu = 0.180610 obj = -1.198455, rho = -0.240458 nSV = 22, nBSV = 14 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *.* optimization finished, #iter = 158 nu = 0.159199 obj = -1.352617, rho = -0.309790 nSV = 20, nBSV = 12 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) *..* optimization finished, #iter = 214 nu = 0.138510 obj = -1.529805, rho = -0.389186 nSV = 19, nBSV = 9 Total nSV = 19 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 88 nu = 0.121255 obj = -1.749225, rho = -0.331938 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.111062 obj = -1.997130, rho = -0.512714 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.099295 obj = -2.271015, rho = -0.401252 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 141 nu = 0.088338 obj = -2.587594, rho = -0.284982 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 92 nu = 0.079093 obj = -2.953957, rho = -0.298853 nSV = 12, nBSV = 4 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.6% (976/1000) (classification) *.* optimization finished, #iter = 124 nu = 0.072131 obj = -3.351281, rho = -0.319523 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 112 nu = 0.065952 obj = -3.763988, rho = -0.292344 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) *.* optimization finished, #iter = 115 nu = 0.061868 obj = -4.133001, rho = -0.251584 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.8% (958/1000) (classification) .*.* optimization finished, #iter = 238 nu = 0.055544 obj = -4.365179, rho = -0.177475 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 100% (100/100) (classification) Accuracy = 96% (960/1000) (classification) ..*.* optimization finished, #iter = 377 nu = 0.048536 obj = -4.488183, rho = -0.192474 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 95.7% (957/1000) (classification) .......* optimization finished, #iter = 796 nu = 0.038496 obj = -4.496217, rho = -0.203951 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .......* optimization finished, #iter = 796 nu = 0.030210 obj = -4.496217, rho = -0.203951 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .......* optimization finished, #iter = 796 nu = 0.023708 obj = -4.496217, rho = -0.203951 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .......* optimization finished, #iter = 796 nu = 0.018605 obj = -4.496217, rho = -0.203951 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .......* optimization finished, #iter = 796 nu = 0.014600 obj = -4.496217, rho = -0.203951 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .......* optimization finished, #iter = 796 nu = 0.011458 obj = -4.496217, rho = -0.203951 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) .......* optimization finished, #iter = 796 nu = 0.008992 obj = -4.496217, rho = -0.203951 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 95.9% (959/1000) (classification) * optimization finished, #iter = 31 nu = 0.209866 obj = -1.462149, rho = -0.163788 nSV = 24, nBSV = 18 Total nSV = 24 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) * optimization finished, #iter = 31 nu = 0.194672 obj = -1.662588, rho = -0.120900 nSV = 22, nBSV = 16 Total nSV = 22 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 100 nu = 0.175044 obj = -1.874865, rho = -0.088472 nSV = 20, nBSV = 14 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.1% (981/1000) (classification) *.* optimization finished, #iter = 113 nu = 0.152784 obj = -2.111476, rho = -0.064965 nSV = 19, nBSV = 10 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 98% (980/1000) (classification) .*.* optimization finished, #iter = 221 nu = 0.132359 obj = -2.396451, rho = -0.043332 nSV = 18, nBSV = 9 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 96 nu = 0.115685 obj = -2.749807, rho = -0.046107 nSV = 16, nBSV = 8 Total nSV = 16 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 183 nu = 0.105687 obj = -3.166481, rho = -0.084706 nSV = 15, nBSV = 8 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 166 nu = 0.098538 obj = -3.616638, rho = -0.153930 nSV = 14, nBSV = 7 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .* optimization finished, #iter = 143 nu = 0.091755 obj = -4.067746, rho = -0.306462 nSV = 13, nBSV = 6 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*.*..* optimization finished, #iter = 375 nu = 0.080369 obj = -4.493733, rho = -0.371275 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 96.7% (967/1000) (classification) ..*..* optimization finished, #iter = 441 nu = 0.069163 obj = -4.986868, rho = -0.403112 nSV = 11, nBSV = 3 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*..* optimization finished, #iter = 308 nu = 0.065920 obj = -5.433029, rho = -0.581743 nSV = 10, nBSV = 3 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ...*...* optimization finished, #iter = 696 nu = 0.059750 obj = -5.617761, rho = -0.693923 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) ....*.....* optimization finished, #iter = 903 nu = 0.048244 obj = -5.634469, rho = -0.643599 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ....*.....* optimization finished, #iter = 903 nu = 0.037860 obj = -5.634469, rho = -0.643599 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ....*.....* optimization finished, #iter = 903 nu = 0.029711 obj = -5.634469, rho = -0.643599 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ....*.....* optimization finished, #iter = 903 nu = 0.023316 obj = -5.634469, rho = -0.643599 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ....*.....* optimization finished, #iter = 903 nu = 0.018297 obj = -5.634469, rho = -0.643599 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ....*.....* optimization finished, #iter = 903 nu = 0.014359 obj = -5.634469, rho = -0.643599 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) ....*.....* optimization finished, #iter = 903 nu = 0.011268 obj = -5.634469, rho = -0.643599 nSV = 11, nBSV = 0 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 98.1% (981/1000) (classification) * optimization finished, #iter = 48 nu = 0.198078 obj = -1.431489, rho = 0.077028 nSV = 25, nBSV = 18 Total nSV = 25 Accuracy = 98% (98/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 28 nu = 0.184769 obj = -1.657721, rho = -0.000253 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) * optimization finished, #iter = 45 nu = 0.163977 obj = -1.914981, rho = 0.071979 nSV = 19, nBSV = 14 Total nSV = 19 Accuracy = 99% (99/100) (classification) Accuracy = 97.8% (978/1000) (classification) * optimization finished, #iter = 27 nu = 0.147894 obj = -2.224902, rho = 0.163916 nSV = 18, nBSV = 13 Total nSV = 18 Accuracy = 99% (99/100) (classification) Accuracy = 97.9% (979/1000) (classification) * optimization finished, #iter = 29 nu = 0.139071 obj = -2.579016, rho = 0.026194 nSV = 17, nBSV = 11 Total nSV = 17 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.131867 obj = -2.932558, rho = -0.079433 nSV = 15, nBSV = 9 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 269 nu = 0.118331 obj = -3.293997, rho = -0.072675 nSV = 16, nBSV = 9 Total nSV = 16 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) .*.* optimization finished, #iter = 291 nu = 0.105722 obj = -3.672539, rho = -0.067047 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 100% (100/100) (classification) Accuracy = 98.2% (982/1000) (classification) *.* optimization finished, #iter = 191 nu = 0.098353 obj = -4.007864, rho = -0.061611 nSV = 14, nBSV = 5 Total nSV = 14 Accuracy = 100% (100/100) (classification) Accuracy = 97.9% (979/1000) (classification) ...*.* optimization finished, #iter = 409 nu = 0.084978 obj = -4.251225, rho = -0.074521 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.8% (978/1000) (classification) ..*.* optimization finished, #iter = 322 nu = 0.069366 obj = -4.513587, rho = -0.089275 nSV = 13, nBSV = 3 Total nSV = 13 Accuracy = 100% (100/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 184 nu = 0.058849 obj = -4.820692, rho = -0.200784 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.6% (976/1000) (classification) .*.* optimization finished, #iter = 207 nu = 0.051354 obj = -5.037481, rho = -0.383241 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .* optimization finished, #iter = 176 nu = 0.041245 obj = -5.218401, rho = -0.369809 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 103 nu = 0.034905 obj = -5.383268, rho = -0.216749 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.028457 obj = -5.396589, rho = -0.136294 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.022332 obj = -5.396589, rho = -0.136294 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.017525 obj = -5.396589, rho = -0.136294 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.013753 obj = -5.396589, rho = -0.136294 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) *.* optimization finished, #iter = 141 nu = 0.010793 obj = -5.396589, rho = -0.136294 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.3% (973/1000) (classification) * optimization finished, #iter = 39 nu = 0.208365 obj = -1.441447, rho = 0.007191 nSV = 24, nBSV = 17 Total nSV = 24 Accuracy = 99% (99/100) (classification) Accuracy = 99% (990/1000) (classification) * optimization finished, #iter = 39 nu = 0.194066 obj = -1.642082, rho = 0.115934 nSV = 21, nBSV = 16 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 99.1% (991/1000) (classification) * optimization finished, #iter = 85 nu = 0.172097 obj = -1.839073, rho = 0.140875 nSV = 21, nBSV = 12 Total nSV = 21 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 89 nu = 0.147142 obj = -2.080761, rho = 0.140967 nSV = 20, nBSV = 11 Total nSV = 20 Accuracy = 99% (99/100) (classification) Accuracy = 98.9% (989/1000) (classification) * optimization finished, #iter = 64 nu = 0.132153 obj = -2.370100, rho = 0.138866 nSV = 17, nBSV = 10 Total nSV = 17 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) * optimization finished, #iter = 74 nu = 0.118126 obj = -2.691770, rho = 0.099538 nSV = 15, nBSV = 7 Total nSV = 15 Accuracy = 99% (99/100) (classification) Accuracy = 98.8% (988/1000) (classification) .* optimization finished, #iter = 163 nu = 0.104493 obj = -3.062061, rho = 0.116114 nSV = 17, nBSV = 7 Total nSV = 17 Accuracy = 98% (98/100) (classification) Accuracy = 98.3% (983/1000) (classification) .*.* optimization finished, #iter = 238 nu = 0.091980 obj = -3.495431, rho = 0.113769 nSV = 15, nBSV = 6 Total nSV = 15 Accuracy = 98% (98/100) (classification) Accuracy = 98% (980/1000) (classification) *.* optimization finished, #iter = 102 nu = 0.083842 obj = -4.002084, rho = 0.104028 nSV = 13, nBSV = 4 Total nSV = 13 Accuracy = 98% (98/100) (classification) Accuracy = 98.4% (984/1000) (classification) *.* optimization finished, #iter = 109 nu = 0.074589 obj = -4.555480, rho = 0.085194 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.5% (985/1000) (classification) .* optimization finished, #iter = 195 nu = 0.065014 obj = -5.235900, rho = 0.066988 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.4% (984/1000) (classification) .*.* optimization finished, #iter = 216 nu = 0.057775 obj = -6.085604, rho = 0.040119 nSV = 12, nBSV = 3 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.2% (982/1000) (classification) ..*...* optimization finished, #iter = 588 nu = 0.052778 obj = -7.086856, rho = 0.027053 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 98.3% (983/1000) (classification) ...* optimization finished, #iter = 384 nu = 0.048241 obj = -8.272231, rho = 0.042162 nSV = 12, nBSV = 2 Total nSV = 12 Accuracy = 99% (99/100) (classification) Accuracy = 97.7% (977/1000) (classification) .* optimization finished, #iter = 183 nu = 0.044720 obj = -9.665879, rho = -0.069870 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.4% (974/1000) (classification) .*.* optimization finished, #iter = 230 nu = 0.043666 obj = -11.094872, rho = -0.308762 nSV = 11, nBSV = 2 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 97.2% (972/1000) (classification) ..*.* optimization finished, #iter = 310 nu = 0.040194 obj = -12.383723, rho = -0.541624 nSV = 11, nBSV = 1 Total nSV = 11 Accuracy = 99% (99/100) (classification) Accuracy = 96.9% (969/1000) (classification) .*.* optimization finished, #iter = 275 nu = 0.036258 obj = -13.657952, rho = -0.896028 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.1% (971/1000) (classification) .*.* optimization finished, #iter = 269 nu = 0.033265 obj = -14.694549, rho = -1.350474 nSV = 10, nBSV = 1 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 97.2% (972/1000) (classification) .*.* optimization finished, #iter = 279 nu = 0.030146 obj = -15.072156, rho = -1.836574 nSV = 10, nBSV = 0 Total nSV = 10 Accuracy = 100% (100/100) (classification) Accuracy = 96.8% (968/1000) (classification)
